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feature-extraction | transformers |
# Model Card for Model ID
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-032e7-1x064-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:18:38+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-128e7-1x064-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:19:08+00:00 |
|
null | transformers |
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## Model Details
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-0.0003_Adam_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:19:53+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. -->
# chatTrained
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "chatTrained", "results": []}]} | DreadN0ugh7/ChatAcademy-Trained-7b | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
] | null | 2024-05-02T21:20:44+00:00 |
feature-extraction | transformers |
# Model Card for Model ID
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- 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.
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[More Information Needed]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-064e7-1x064-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:21:50+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-256e7-1x064-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:21:51+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **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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| {"library_name": "transformers", "tags": []} | shallow6414/x4bnnkc | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:21:52+00:00 |
null | null | {"license": "apache-2.0"} | JeffersonMusic/MJInivincibleEra2001V2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T21:21:53+00:00 |
|
sentence-similarity | sentence-transformers |
# seregadgl101/baii_v6_10ep
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('seregadgl101/baii_v6_10ep')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=seregadgl101/baii_v6_10ep)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | seregadgl101/baii_v6_10ep | null | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:22:09+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Alvaroooooooo/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | Alvaroooooooo/ppo-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2024-05-02T21:22:34+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-3e-05_SGD_M_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:22:54+00:00 |
null | 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. -->
# test_ViT-Masked_2
This model is a fine-tuned version of [](https://huggingface.co/) on the ppak10/Melt-Pool-Thermal-Images 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: 1024
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "test_ViT-Masked_2", "results": []}]} | ppak10/test_ViT-Masked_2 | null | [
"transformers",
"safetensors",
"vit",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:23:13+00:00 |
null | null | {"license": "apache-2.0"} | Ashima/setence_level_llama2_7b | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T21:23:14+00:00 |
|
null | null | {"license": "openrail"} | gaizerick/pes13 | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T21:23:32+00:00 |
|
text-to-image | diffusers | ### arrcr Dreambooth model trained by aikonst2024 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | aikonst2024/arrcr | null | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-05-02T21:23:36+00:00 |
text-generation | transformers | {"license": "cc"} | qud-parsing/sentence_level_llama2_7b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:23:59+00:00 |
|
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Sao10K/NyakuraV2-34B-Yi-Llama
| {} | mradermacher/NyakuraV2-34B-Yi-Llama-i1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-02T21:24:02+00:00 |
text-generation | transformers | ## Model description
This is just a mirror of Yi-34B-200K (XLCTX) model uploaded by 01.ai. This is the version that was updated to work better with long context needle in a haystack recall. I just want to have it stored somewhere in case 01.ai updates the base Yi-34B-200K model again.\
It can be used as a base to merge loras into.
| {"license": "other", "license_name": "yi-license", "license_link": "LICENSE"} | adamo1139/Yi-34B-200K-XLCTX | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:26:01+00:00 |
text-generation | transformers | ## Model description
This is a base Yi-34B-200K XLCTX model treated with DPO with adamo1139/rawrr_v2-2_stage1 dataset to make outputs be completions instead of answers for a question. DPO was done using chatml format but no previous SFT step was done. If it would do it now, I would have used ORPO instead of DPO for this step to make it stronger, but too late for that. It can be used to maybe slightly decensor a model, but I don't think this idea works too well with DPO before SFT step, as was widely known but I did it anyway.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" alt="made with Unsloth" width="400" height="64"/>](https://github.com/unslothai/unsloth)
## Training script for Unsloth
```
from unsloth import FastLanguageModel
from datasets import Dataset, load_dataset
from dataclasses import dataclass, field
from typing import Dict, Optional
import torch
max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "adamo1139/Yi-34B-200K-XLCTX", # Choose ANY! eg mistralai/Mistral-7B-Instruct-v0.2
max_seq_length = max_seq_length,
attn_implementation="flash_attention_2",
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
#@title Alignment Handbook utils
import os
import re
from typing import List, Literal, Optional
from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
from datasets.builder import DatasetGenerationError
#DEFAULT_CHAT_TEMPLATE = "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}"
tokenizer.chat_template = "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
EOS_TOKEN = tokenizer.eos_token
def chatml_format(example):
# Format system
if len(example['system']) > 0:
message = {"role": "system", "content": example['system']}
system = tokenizer.apply_chat_template([message], tokenize=False)
else:
system = ""
# Format instruction
message = {"role": "user", "content": example['prompt']}
prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True)
# Format chosen answer
chosen = example['chosen'] + "<|im_end|>\n" + EOS_TOKEN
# Format rejected answer
rejected = example['rejected'] + "<|im_end|>\n" + EOS_TOKEN
return {
"prompt": system + prompt,
"chosen": chosen,
"rejected": rejected,
}
# Load dataset
dataset = load_dataset("adamo1139/rawrr_v2-2_stage1", split="train")
import pprint
pprint.pprint("""NOT a formatted dataset
""")
pprint
pprint.pprint(dataset[250])
pprint.pprint(dataset[260])
pprint.pprint(dataset[270])
pprint.pprint(dataset[280])
pprint.pprint(dataset[290])
# Save columns
original_columns = dataset.column_names
# Format dataset
dataset = dataset.map(
chatml_format,
remove_columns=original_columns
)
# Print sample
pprint.pprint("""formatted dataset""")
pprint.pprint(dataset[250])
pprint.pprint(dataset[260])
pprint.pprint(dataset[270])
pprint.pprint(dataset[280])
pprint.pprint(dataset[290])
model = FastLanguageModel.get_peft_model(
model,
r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 32,
lora_dropout = 0, # Currently only supports dropout = 0
bias = "none", # Currently only supports bias = "none"
use_gradient_checkpointing = "unsloth",
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
from trl import DPOTrainer
dpo_trainer = DPOTrainer(
model = model,
ref_model = None,
args = TrainingArguments(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 16,
warmup_ratio = 0.03,
num_train_epochs = 1,
learning_rate = 0.0001,
fp16 = not torch.cuda.is_bf16_supported(),
bf16 = torch.cuda.is_bf16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.0,
lr_scheduler_type = "cosine",
seed = 42,
save_strategy = "steps",
save_steps = 100,
save_total_limit = 20,
output_dir = "1904-yi-200k-xlctx-raw-intermediate",
),
beta = 0.1,
train_dataset = dataset,
# eval_dataset = raw_datasets["test"],
tokenizer = tokenizer,
max_length = 650,
max_prompt_length = 650,
)
dpo_trainer.train()
model.save_pretrained("1904-yi-200k-xlctx-raw-final") # Local saving
``` | {"license": "other", "datasets": ["adamo1139/rawrr_v2-2_stage1"], "license_name": "yi-license", "license_link": "LICENSE"} | adamo1139/Yi-34B-200K-XLCTX-RAW-1904 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:adamo1139/rawrr_v2-2_stage1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:27:05+00:00 |
text-generation | transformers |
## Model description
Yi-34B 200K XLCTX base model fine-tuned on adamo1139/rawrr_v2-2_stage1 (DPO), adamo1139/AEZAKMI_v3-7 (SFT) and adamo1139/toxic-dpo-natural-v5 (ORPO) datasets. Training took around 7 (DPO) + 13 (SFT) + 3 (ORPO) = 23 hours total on RTX 3090 Ti, all finetuning was done locally. This is excluding failed attempts and issues I had with merging script, that basically made me run DPO and SFT stages 2 times over because I thought that my LoRAs were broken, but it turned out to be some bug with new transformers/peft versions.
This model is tuned to use more natural language and also be very uncensored.
Say goodbye to "It's important to remember"! \
Prompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.
Cost of this fine-tune is about $5-$10 in electricity.
Base model used for fine-tuning was Yi-34B-200K model shared by 01.ai, the newer version that has improved long context needle in a haystack retrieval. They didn't give it a new name, giving it numbers would mess up AEZAKMI naming scheme by adding a second number, so I will be calling it XLCTX.
[You can see examples of responses to various prompts here (loaded with transformers load_in_4bit)](https://huggingface.co/datasets/adamo1139/misc/blob/main/benchmarks/yi-34b-200k-xlctx-aezakmi-raw-toxic-natural-orpo-0205/benchmark_prompts.txt)
I had to lower max_positional_embeddings in config.json and model_max_length for training to start, otherwise I was OOMing straight away.
This attempt had both max_position_embeddings and model_max_length set to 4096, which worked perfectly fine. I then reversed this to 200000 once I was uploading it.
I think it should keep long context capabilities of the base model should be present here.
If you want to see training scripts, let me know and I will upload them. LoRAs are uploaded [here adamo1139/yi-34b-200k-xlctx-aezakmi-raw-toxic-dpo-sft-orpo-lora-0205](https://huggingface.co/adamo1139/yi-34b-200k-xlctx-aezakmi-raw-toxic-dpo-sft-orpo-lora-0205)
## Quants!
EXL2 quant coming soon, I plan to make and upload something around 4.65bpw, it should work nicely with q4 cache in exllama2
## Prompt Format
I recommend using ChatML format, as this was used during fine-tune. \
Here's a prompt format you should use, you can set a different system message, model was trained on SystemChat dataset, so it should respect system prompts fine.
```
<|im_start|>system
A chat.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Intended uses & limitations
Use is limited by Yi license. \
Some datasets that were used prohibit commercial use (no_robots with CC-BY-NC-4.0), so I think you should use non-commercially only, unless you know law better and think it doesn't matter.
## Known Issues
I haven't found any yet.
## Credits
Thanks to unsloth and huggingface team for providing software packages used during fine-tuning. \
Thanks to Jon Durbin, abacusai, huggingface, sandex, NobodyExistsOnTheInternet, Nous-Research, lmsys, PygmalionAI for open sourcing datasets I included in the AEZAKMI dataset. \
AEZAKMI is basically a mix of open source datasets I found on HF, so without them this would not be possible at all.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" alt="made with Unsloth" width="400" height="64"/>](https://github.com/unslothai/unsloth)
| {"license": "other", "datasets": ["adamo1139/toxic-dpo-natural-v5", "adamo1139/AEZAKMI_v3-7", "adamo1139/rawrr_v2-2_stage1"], "license_name": "yi-license", "license_link": "LICENSE"} | adamo1139/Yi-34B-200K-XLCTX-AEZAKMI-RAW-TOXIC-0205 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:adamo1139/toxic-dpo-natural-v5",
"dataset:adamo1139/AEZAKMI_v3-7",
"dataset:adamo1139/rawrr_v2-2_stage1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:27:45+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | tjluyao/llama-3-8b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:27:59+00:00 |
text-generation | transformers | {} | fabriceyhc/Meta-Llama-3-8B-Instruct-DrugDetection | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:29:11+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 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-008e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:29:58+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-032e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:30:52+00:00 |
|
null | null | {} | phuong123/envit5-translation-term-med-fine-tune | null | [
"region:us"
] | null | 2024-05-02T21:31:40+00:00 |
|
null | null | {"license": "openrail"} | Homiebear/Baymax | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T21:31:47+00:00 |
|
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-064e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:32:52+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model46 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:33:05+00:00 |
feature-extraction | transformers |
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-016e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:33:14+00:00 |
null | null | {} | WALIDALI/AbsurdMonolithLandscapes | null | [
"region:us"
] | null | 2024-05-02T21:33:16+00:00 |
|
automatic-speech-recognition | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Mihaj/whisper-small-karelian-CodeSwitching_with_tempo_aug | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:33:33+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | shallow6414/xrlklu6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:33:53+00:00 |
null | null | {} | phuong123/opus-mt-en-vi-med-term-fine-tune | null | [
"region:us"
] | null | 2024-05-02T21:33:54+00:00 |
|
sentence-similarity | sentence-transformers |
# seregadgl101/baii_v6_12ep
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('seregadgl101/baii_v6_12ep')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=seregadgl101/baii_v6_12ep)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | seregadgl101/baii_v6_12ep | null | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:34:24+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. -->
# llava_13b_country
This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- 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: 0.03
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8996 | 1.0 | 9 | 1.0150 |
| 0.2338 | 2.0 | 18 | 1.1114 |
| 0.0743 | 3.0 | 27 | 1.1836 |
| 0.0278 | 4.0 | 36 | 1.4034 |
| 0.0122 | 5.0 | 45 | 1.4664 |
| 0.0129 | 6.0 | 54 | 1.4691 |
| 0.0062 | 7.0 | 63 | 1.4761 |
| 0.0026 | 8.0 | 72 | 1.4731 |
| 0.0018 | 9.0 | 81 | 1.6151 |
| 0.0012 | 10.0 | 90 | 1.6820 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "liuhaotian/llava-v1.5-13b", "model-index": [{"name": "llava_13b_country", "results": []}]} | emendes3/llava_13b_country | null | [
"peft",
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:liuhaotian/llava-v1.5-13b",
"4-bit",
"region:us"
] | null | 2024-05-02T21:34:27+00:00 |
text-generation | transformers | {} | Tristan/pythia-70m-en-language-as-domain | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:34:44+00:00 |
|
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_v23e-05_SGD_M | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:34:55+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/xp7khfs | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:36:14+00:00 |
null | null | {} | golf2248/e49rtcm | null | [
"region:us"
] | null | 2024-05-02T21:36:44+00:00 |
|
null | transformers | {"license": "apache-2.0"} | nccratliri/whisperseg-marmoset-ct2 | null | [
"transformers",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:36:50+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | golf2248/67f3y6q | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:37:34+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/bunnycore/LuminariX-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LuminariX-8B-GGUF/resolve/main/LuminariX-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit"], "base_model": "bunnycore/LuminariX-8B", "quantized_by": "mradermacher"} | mradermacher/LuminariX-8B-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:bunnycore/LuminariX-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:37:55+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_v20.0003_Adam | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:39:01+00:00 |
null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "Qwen/Qwen1.5-0.5B-Chat"} | asbabiy/AspectLens-BA-Tiny | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B-Chat",
"region:us"
] | null | 2024-05-02T21:39:29+00:00 |
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | cilantro9246/89tlt1o | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:39:32+00:00 |
null | mlx |
# lostairpod/Llama3-OpenBioLLM-8B-MLX
This model was converted to MLX format from [`aaditya/Llama3-OpenBioLLM-8B`](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) using mlx-lm version **0.0.13**.
Model added by [lostairpod]
Refer to the [original model card](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("lostairpod/Llama3-OpenBioLLM-8B-MLX")
response = generate(model, tokenizer, prompt="hello", verbose=True)
``` | {"language": ["en"], "license": "llama3", "tags": ["mlx"]} | lostairpod/Llama3-OpenBioLLM-8B-MLX | null | [
"mlx",
"safetensors",
"llama",
"en",
"license:llama3",
"region:us"
] | null | 2024-05-02T21:39:39+00:00 |
null | null | {} | largenumber/Test2 | null | [
"region:us"
] | null | 2024-05-02T21:39:42+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | swj0419/random_7b-5-2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:40:50+00:00 |
text-to-image | diffusers |
# AutoTrain SDXL LoRA DreamBooth - kpal002/Dreambooth-SDXL
<Gallery />
## Model description
These are kpal002/Dreambooth-SDXL 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: None.
## Trigger words
You should use A portrait in a distinctive expressive style characterized by vivid color use, emotional depth, and dynamic brush strokes, capturing the nuanced expressions of children and teenagers. to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kpal002/Dreambooth-SDXL/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A portrait in a distinctive expressive style characterized by vivid color use, emotional depth, and dynamic brush strokes, capturing the nuanced expressions of children and teenagers."} | kpal002/Dreambooth-SDXL | null | [
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-05-02T21:41:00+00:00 |
null | null | {} | Aa123564/zephyr-7b-sft-qlora | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-05-02T21:41:24+00:00 |
|
null | null | {"license": "openrail"} | itskeonagain/JulieHan | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T21:42:27+00:00 |
|
text-generation | transformers | {} | Zekunli/Llama-2-7b-mlp2x-mmncall | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:42:40+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** prasanthg3
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | prasanthg3/finance-llama-3-8b | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:42:53+00:00 |
text-generation | transformers | Wiggle merge of aaditya/Llama3-OpenBioLLM-8B into Orenguteng/Llama-3-8B-Lexi-Uncensored
| {"license": "llama3"} | athirdpath/Llama-3-15b-OpenBioLexi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:43:08+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[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]
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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
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | coming-san-fran/open-bit | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:44:04+00:00 |
null | null | {} | snakesss/godzila | null | [
"region:us"
] | null | 2024-05-02T21:44:21+00:00 |
|
null | null | {} | rinapch/phi1.5-kotlin | null | [
"safetensors",
"region:us"
] | null | 2024-05-02T21:44:29+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. -->
# llava_13b_city
This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6347
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- 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: 0.03
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8986 | 1.0 | 9 | 1.0144 |
| 0.2358 | 2.0 | 18 | 1.1164 |
| 0.0721 | 3.0 | 27 | 1.1954 |
| 0.0289 | 4.0 | 36 | 1.3896 |
| 0.0089 | 5.0 | 45 | 1.4742 |
| 0.0073 | 6.0 | 54 | 1.4845 |
| 0.0051 | 7.0 | 63 | 1.5379 |
| 0.0012 | 8.0 | 72 | 1.5769 |
| 0.0015 | 9.0 | 81 | 1.6411 |
| 0.0005 | 10.0 | 90 | 1.6347 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "liuhaotian/llava-v1.5-13b", "model-index": [{"name": "llava_13b_city", "results": []}]} | emendes3/llava_13b_city | null | [
"peft",
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:liuhaotian/llava-v1.5-13b",
"4-bit",
"region:us"
] | null | 2024-05-02T21:45:27+00:00 |
null | null | {} | ivykopal/spanish_prompt_100k | null | [
"region:us"
] | null | 2024-05-02T21:46:38+00:00 |
|
null | null | {} | Traxap/Bias_Mitigation_LoRA | null | [
"region:us"
] | null | 2024-05-02T21:48:10+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | shallow6414/h2tpy9a | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:48:16+00:00 |
text-generation | transformers | Reproducing
(https://huggingface.co/blog/mlabonne/orpo-llama-3
)[https://huggingface.co/blog/mlabonne/orpo-llama-3]
while extending training to 10 epochs. | {} | SaborDay/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:49:00+00:00 |
text-generation | transformers | {} | Alex-dc/Meta-Llama-3-8B-french-subtitles | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:51:58+00:00 |
|
text-generation | transformers | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama"], "base_model": "meta-llama/Meta-Llama-3-8B"} | predibase/Meta-Llama-3-8B-dequantized | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:52:03+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. -->
# results
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: 1.5800
- Bleu: 0.0046
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.2
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 1.662 | 1.0 | 782 | 1.6355 | 0.0046 | 19.0 |
| 1.5472 | 2.0 | 1564 | 1.6352 | 0.0 | 19.0 |
| 1.516 | 3.0 | 2346 | 1.5800 | 0.0046 | 19.0 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "model-index": [{"name": "results", "results": []}]} | omarmedhatt/results | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:52:20+00:00 |
text-classification | transformers | # Claim-Evidence Alignment TinyBERT tuned classification model
<!-- Provide a quick summary of what the model is/does. -->
This repo contains a tuned [huawei-noah/TinyBERT_General_4L_312D](https://huggingface.co/huawei-noah/TinyBERT_General_4L_312D) model for the classification
of sentence pairs: if the evidence fits the claim. For the training, the following dataset was used: [copenlu/fever_gold_evidence](https://huggingface.co/datasets/copenlu/fever_gold_evidence).
The model is trained on both test and train datasets.
## Usage
```python
model = transformers.AutoModelForSequenceClassification.from_pretrained("yevhenkost/claim_evidence_alignment_fever_gold_tuned_tinybert")
tokenizer = transformers.AutoTokenizer.from_pretrained("yevhenkost/claim_evidence_alignment_fever_gold_tuned_tinybert")
claim_evidence_pairs = [
["The water is wet", "The sky is blue"],
["The car crashed", "Driver could not see the road"]
]
tokenized_inputs = tokenizer.batch_encode_plus(
predict_pairs,
return_tensors="pt",
padding=True,
truncation=True
)
preds = model(**tokenized_batch_input)
# logits: preds.logits
# 0 - Not aligned;
1 - aligned
```
## Dataset Processing
The dataset was processed in the following way:
```python
import os
from sklearn.model_selection import train_test_split
claims, evidences, labels = [], [], []
# LOADED WITH THE HUGGINGFACE HUB INTO JSONL FORMAT
datadir = "copenlu_fever_gold_evidence/"
for filename in os.listdir(datadir):
with open(os.path.join(datadir, filename), "r") as f:
for line in f.read().split("\n"):
if line:
row_dict = json.loads(line)
for evidence in row_dict["evidence"]:
evidences.append(evidence[-1])
claims.append(row_dict["claim"])
if row_dict["label"] != "NOT ENOUGH INFO":
labels.append(1)
else:
labels.append(0)
df = pd.DataFrame()
df["text_a"] = claims
df["text_b"] = evidences
df["labels"] = labels
df = df.drop_duplicates(subset=["text_a", "text_b"])
train_df, eval_df = train_test_split(df, random_state=2, test_size=0.2)
```
### Metrics
```
precision recall f1-score support
0 0.86 0.60 0.71 15958
1 0.86 0.96 0.91 42327
accuracy 0.86 58285
macro avg 0.86 0.78 0.81 58285
weighted avg 0.86 0.86 0.85 58285
```
| {"language": ["en"], "tags": ["evidence", "claim", "evidence alignment"]} | yevhenkost/claim_evidence_alignment_fever_gold_tuned_tinybert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"evidence",
"claim",
"evidence alignment",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:52:55+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nextab/Athena-v2.0-sft
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v2.0-sft-GGUF/resolve/main/Athena-v2.0-sft.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "base_model": "nextab/Athena-v2.0-sft", "quantized_by": "mradermacher"} | mradermacher/Athena-v2.0-sft-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:nextab/Athena-v2.0-sft",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:54:30+00:00 |
null | null | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.com/invite/vb6SmA3hxu)
## This repo contains GGUF versions of the mlabonne/ChimeraLlama-3-8B-v2 model.
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with GGUF.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***What is the model format?*** We use GGUF format.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
# Downloading and running the models
You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/):
| Quant type | Description |
|------------|--------------------------------------------------------------------------------------------|
| Q5_K_M | High quality, recommended. |
| Q5_K_S | High quality, recommended. |
| Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. |
| Q4_K_S | Slightly lower quality with more space savings, recommended. |
| IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. |
| IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Q3_K_L | Lower quality but usable, good for low RAM availability. |
| Q3_K_M | Even lower quality. |
| IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| Q3_K_S | Low quality, not recommended. |
| IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| Q2_K | Very low quality but surprisingly usable. |
## How to download GGUF files ?
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
- **Option A** - Downloading in `text-generation-webui`:
- **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/ChimeraLlama-3-8B-v2-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf.
- **Step 2**: Then click Download.
- **Option B** - Downloading on the command line (including multiple files at once):
- **Step 1**: We recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
- **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download PrunaAI/ChimeraLlama-3-8B-v2-GGUF-smashed ChimeraLlama-3-8B-v2.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
Alternatively, you can also download multiple files at once with a pattern:
```shell
huggingface-cli download PrunaAI/ChimeraLlama-3-8B-v2-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/ChimeraLlama-3-8B-v2-GGUF-smashed ChimeraLlama-3-8B-v2.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## How to run model in GGUF format?
- **Option A** - Introductory example with `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m ChimeraLlama-3-8B-v2.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
- **Option B** - Running in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).
- **Option C** - Running from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./ChimeraLlama-3-8B-v2.IQ3_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./ChimeraLlama-3-8B-v2.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
- **Option D** - Running with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
| {"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"} | PrunaAI/ChimeraLlama-3-8B-v2-GGUF-smashed | null | [
"gguf",
"pruna-ai",
"region:us"
] | null | 2024-05-02T21:54:56+00:00 |
null | null | {} | Prokoblin/Idk | null | [
"region:us"
] | null | 2024-05-02T21:56:02+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/ihsfy25 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T21:56:20+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)
Atom-7B-Chat - GGUF
- Model creator: https://huggingface.co/FlagAlpha/
- Original model: https://huggingface.co/FlagAlpha/Atom-7B-Chat/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Atom-7B-Chat.Q2_K.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q2_K.gguf) | Q2_K | 2.5GB |
| [Atom-7B-Chat.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.IQ3_XS.gguf) | IQ3_XS | 2.76GB |
| [Atom-7B-Chat.IQ3_S.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.IQ3_S.gguf) | IQ3_S | 2.9GB |
| [Atom-7B-Chat.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q3_K_S.gguf) | Q3_K_S | 2.9GB |
| [Atom-7B-Chat.IQ3_M.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [Atom-7B-Chat.Q3_K.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q3_K.gguf) | Q3_K | 3.23GB |
| [Atom-7B-Chat.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q3_K_M.gguf) | Q3_K_M | 3.23GB |
| [Atom-7B-Chat.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q3_K_L.gguf) | Q3_K_L | 3.51GB |
| [Atom-7B-Chat.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.IQ4_XS.gguf) | IQ4_XS | 3.57GB |
| [Atom-7B-Chat.Q4_0.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q4_0.gguf) | Q4_0 | 3.74GB |
| [Atom-7B-Chat.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.IQ4_NL.gguf) | IQ4_NL | 3.76GB |
| [Atom-7B-Chat.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q4_K_S.gguf) | Q4_K_S | 3.77GB |
| [Atom-7B-Chat.Q4_K.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q4_K.gguf) | Q4_K | 3.98GB |
| [Atom-7B-Chat.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q4_K_M.gguf) | Q4_K_M | 3.98GB |
| [Atom-7B-Chat.Q4_1.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q4_1.gguf) | Q4_1 | 4.13GB |
| [Atom-7B-Chat.Q5_0.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q5_0.gguf) | Q5_0 | 4.52GB |
| [Atom-7B-Chat.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q5_K_S.gguf) | Q5_K_S | 4.52GB |
| [Atom-7B-Chat.Q5_K.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q5_K.gguf) | Q5_K | 4.65GB |
| [Atom-7B-Chat.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q5_K_M.gguf) | Q5_K_M | 4.65GB |
| [Atom-7B-Chat.Q5_1.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q5_1.gguf) | Q5_1 | 4.92GB |
| [Atom-7B-Chat.Q6_K.gguf](https://huggingface.co/RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf/blob/main/Atom-7B-Chat.Q6_K.gguf) | Q6_K | 5.36GB |
Original model description:
---
developers: [https://huggingface.co/FlagAlphaAI]
license: apache-2.0
language:
- zh
- en
pipeline_tag: question-answering
library_name: transformers
---
# Atom-7B-32k-Chat
基于Atom-7B具有32k长度的对话模型，完全开源可商用，由Llama中文社区和AtomEcho（原子回声）联合研发，基于Llama2-7B采用大规模的中文数据进行了继续预训练，我们会持续提供更新的模型参数，模型训练过程见[llama.family](https://llama.family)。
模型的部署、训练、微调等方法详见Llama中文社区GitHub仓库：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)。
## 📝 中文数据
| 类型 | 描述 |
| ---------------------------------------------------------- | ------------------------------------------------------------ |
| 网络数据 | 互联网上公开的网络数据，挑选出去重后的高质量中文数据，涉及到百科、书籍、博客、新闻、公告、小说等高质量长文本数据。 |
| [Wikipedia](https://github.com/goldsmith/Wikipedia) | 中文Wikipedia的数据 |
| [悟道](https://github.com/BAAI-WuDao/Model) | 中文悟道开源的200G数据 |
| [Clue](https://github.com/CLUEbenchmark/CLUEDatasetSearch) | Clue开放的中文预训练数据，进行清洗后的高质量中文长文本数据 |
| 竞赛数据集 | 近年来中文自然语言处理多任务竞赛数据集，约150个 |
| [MNBVC](https://github.com/esbatmop/MNBVC) | MNBVC 中清洗出来的部分数据集 |
**我们也欢迎大家在[llama.family](https://llama.family)中贡献自己的数据，您的数据通过审核后会加入模型训练，也将影响模型未来的能力走向。**
## 📚 中文词表
为了提高中文文本处理的效率，我们针对Llama2模型的词表进行了深度优化。
首先，我们基于数百G的中文文本，**在Llama2词表的基础上扩展词库至65,000个单词**。
经过测试，我们的改进使得**中文编码/解码速度提高了约350％**。
此外，我们还扩大了中文字符集的覆盖范围，包括所有**emoji符号**，这使的生成带有表情符号的文章更加高效。
对于Llama2原生词表中的一些特殊情况，如数字、英文等，我们尽可能地避免对其进行修改或替换。
最终，成功地实现了一种既能提高中文处理效率又能保持Llama2原有性能的方法。
## 📈 训练过程
**模型结构**
基于当前最优秀的开源模型Llama2，使用主流Decoder-only的标准Transformer网络结构，支持4K的上下文长度（Context Length），为同尺寸模型中最长，能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
**FlashAttention-2高效训练**
Atom-7B采用了FlashAttention-2技术进行训练。由于在处理较长的输入序列时，内存消耗的问题可能会导致“内存爆炸”现象。FlashAttention-2是一种高效注意力机制的实现方式之一，相较于传统的注意力技术（Attention），它拥有更快速的速度以及更加优化的内存占用率。
**基于NTK的自适应上下文扩展技术**
- 可在不继续训练模型的情况下支持更长的上下文
- 本项目中模型默认支持4K上下文，利用上述技术可扩展至18K+
- 经过微调可以支持到32K+
## 💻 推理配置
实际应用中，消费级显卡要比专业显卡便宜的多（比如3090相比A10，同样都是24G显存）。
对于消费级显卡，直接FP32肯定放不下，一般最基本的是FP16，而INT8和INT4量化就很有用，例如：
- 对于3080显卡（10G显存），Atom-7B的INT8只需要8G显存可以直接部署。
- 对于3080显卡（10G显存），Atom-7B的INT4只需要5G显存可以直接部署。
---
# Llama中文社区
## 🚀 社区地址：
Github：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)
在线体验链接：[**llama.family**](https://llama.family/)
## 🔥 社区介绍
欢迎来到Llama中文社区！
我们是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。
**基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级**。
我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。
## 🐼 社区资源
- Llama2在线体验链接[**llama.family**](https://llama.family/)，同时包含Meta原版和中文微调版本！
- Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！
- [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
| {} | RichardErkhov/FlagAlpha_-_Atom-7B-Chat-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T21:57:18+00:00 |
null | mlx |
# lostairpod/Llama3-OpenBioLLM-8B-MLX
This model was converted to MLX format from [`aaditya/Llama3-OpenBioLLM-8B`](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) using mlx-lm version **0.0.13**.
Model added by [lostairpod]
Refer to the [original model card](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama3-OpenBioLLM-8B")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "llama3", "tags": ["mlx"]} | mlx-community/Llama3-OpenBioLLM-8B | null | [
"mlx",
"safetensors",
"llama",
"en",
"license:llama3",
"region:us"
] | null | 2024-05-02T21:57:46+00:00 |
null | null | {"license": "llama3"} | Franckh555/llama3 | null | [
"license:llama3",
"region:us"
] | null | 2024-05-02T21:58:55+00:00 |
|
fill-mask | transformers |
## Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|--------------------------|---------------------------------|
| Duration (in seconds) | [More Information Needed] |
| Emissions (Co2eq in kg) | [More Information Needed] |
| CPU power (W) | [NO CPU] |
| GPU power (W) | [No GPU] |
| RAM power (W) | [More Information Needed] |
| CPU energy (kWh) | [No CPU] |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | [More Information Needed] |
| Consumed energy (kWh) | [More Information Needed] |
| Country name | [More Information Needed] |
| Cloud provider | [No Cloud] |
| Cloud region | [No Cloud] |
| CPU count | [No CPU] |
| CPU model | [No CPU] |
| GPU count | [No GPU] |
| GPU model | [No GPU] |
## Environmental Impact (for one core)
| Metric | Value |
|--------------------------|---------------------------------|
| CPU energy (kWh) | [No CPU] |
| Emissions (Co2eq in kg) | [More Information Needed] |
## Note
2 May 2024
## My Config
| Config | Value |
|--------------------------|-----------------|
| checkpoint | albert-base-v2 |
| model_name | BERTrand_bs64_lr5_MLM |
| sequence_length | 400 |
| num_epoch | 12 |
| learning_rate | 5e-05 |
| batch_size | 64 |
| weight_decay | 0.0 |
| warm_up_prop | 0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 3270 |
## Training and Testing steps
Epoch | Train Loss | Test Loss
---|---|---
| 0.0 | 14.601907 | 13.069239 |
| 0.5 | 7.447329 | 6.978540 |
| 1.0 | 6.960886 | 6.950151 |
| 1.5 | 4.932429 | 2.806009 |
| 2.0 | 2.701225 | 2.655897 |
| 2.5 | 2.606577 | 2.581771 |
| 3.0 | 2.548158 | 2.557307 |
| 3.5 | 2.505096 | 2.519658 |
| 4.0 | 2.481079 | 2.493212 |
| {"language": "en", "tags": ["fill-mask"]} | damgomz/BERTrand_bs64_lr5_MLM | null | [
"transformers",
"safetensors",
"albert",
"fill-mask",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T21:59:25+00:00 |
text-generation | transformers |
# Model Card for Model ID
## Model Details
### Model Description
Korean minimal instruction tunning of meta-llama/Meta-Llama-3-8B-Instruct
#### Chat template
tokenizer.apply_chat_template(chat, tokenize=False)
| {"language": ["ko"], "license": "apache-2.0", "library_name": "transformers"} | lcw99/llama-3-8b-it-kor-extented-chang | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:00:09+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. -->
# llava_13b_exact_location_name
This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- 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: 0.03
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.895 | 1.0 | 9 | 1.0173 |
| 0.2263 | 2.0 | 18 | 1.1246 |
| 0.0698 | 3.0 | 27 | 1.1885 |
| 0.0285 | 4.0 | 36 | 1.3967 |
| 0.0085 | 5.0 | 45 | 1.4823 |
| 0.0062 | 6.0 | 54 | 1.4699 |
| 0.0027 | 7.0 | 63 | 1.4972 |
| 0.0033 | 8.0 | 72 | 1.5496 |
| 0.0016 | 9.0 | 81 | 1.4242 |
| 0.0037 | 10.0 | 90 | 1.4214 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "liuhaotian/llava-v1.5-13b", "model-index": [{"name": "llava_13b_exact_location_name", "results": []}]} | emendes3/llava_13b_exact_location_name | null | [
"peft",
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:liuhaotian/llava-v1.5-13b",
"4-bit",
"region:us"
] | null | 2024-05-02T22:00:14+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/0wugr63 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:02:52+00:00 |
text-generation | transformers |
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[More Information Needed]
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| {"library_name": "transformers", "tags": []} | golf2248/r4iwokv | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:03:09+00:00 |
text-generation | transformers |
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | shallow6414/lwodnjp | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:03:43+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. -->
# japanese-sentiment
This model is a fine-tuned version of [abhishek/autonlp-japanese-sentiment-59363](https://huggingface.co/abhishek/autonlp-japanese-sentiment-59363) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "abhishek/autonlp-japanese-sentiment-59363", "model-index": [{"name": "japanese-sentiment", "results": []}]} | conrad123/japanese-sentiment | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:abhishek/autonlp-japanese-sentiment-59363",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T22:05:20+00:00 |
text-generation | transformers |
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## 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).
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| {"library_name": "transformers", "tags": []} | cilantro9246/sf3y59y | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:06:17+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/kloodia/llama3-8x8-chat
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q2_K.gguf) | Q2_K | 21.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.IQ3_XS.gguf) | IQ3_XS | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q3_K_S.gguf) | Q3_K_S | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.IQ3_S.gguf) | IQ3_S | 25.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.IQ3_M.gguf) | IQ3_M | 26.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q3_K_M.gguf) | Q3_K_M | 28.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q3_K_L.gguf) | Q3_K_L | 30.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.IQ4_XS.gguf) | IQ4_XS | 31.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q4_K_S.gguf) | Q4_K_S | 33.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q4_K_M.gguf) | Q4_K_M | 35.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q5_K_S.gguf) | Q5_K_S | 40.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q5_K_M.gguf) | Q5_K_M | 41.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q6_K.gguf) | Q6_K | 48.3 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/llama3-8x8-chat-GGUF/resolve/main/llama3-8x8-chat.Q8_0.gguf.part2of2) | Q8_0 | 62.5 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "base_model": "kloodia/llama3-8x8-chat", "quantized_by": "mradermacher"} | mradermacher/llama3-8x8-chat-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:kloodia/llama3-8x8-chat",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T22:06:59+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5530
- eval_runtime: 80.7835
- eval_samples_per_second: 2.488
- eval_steps_per_second: 2.488
- epoch: 1.9737
- step: 750
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 10
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceM4/idefics2-8b", "model-index": [{"name": "gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned", "results": []}]} | gimarchetti/gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned | null | [
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T22:09:22+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## 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 [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | massimilianowosz/OrpoLlama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:09:25+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-i1-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-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF/resolve/main/Llama-3-Lumimaid-70B-v0.1.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "nsfw"], "base_model": "NeverSleep/Llama-3-Lumimaid-70B-v0.1", "quantized_by": "mradermacher"} | mradermacher/Llama-3-Lumimaid-70B-v0.1-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"nsfw",
"en",
"base_model:NeverSleep/Llama-3-Lumimaid-70B-v0.1",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T22:09:32+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. -->
# bn_news_article_summarization
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1868
## 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: 0.001
- train_batch_size: 20
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 160
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.99 | 83 | 0.5499 |
| No log | 2.0 | 167 | 0.6308 |
| 16.0025 | 2.99 | 250 | 0.4921 |
| 16.0025 | 3.99 | 334 | 0.4755 |
| 0.5191 | 5.0 | 418 | 0.4795 |
| 0.5191 | 5.99 | 501 | 0.4719 |
| 0.5191 | 7.0 | 585 | 0.4575 |
| 0.4756 | 8.0 | 669 | 0.4345 |
| 0.4756 | 8.99 | 752 | 0.3438 |
| 0.4157 | 10.0 | 836 | 0.2664 |
| 0.4157 | 10.99 | 919 | 0.2263 |
| 0.2776 | 11.99 | 1003 | 0.2013 |
| 0.2776 | 13.0 | 1087 | 0.1899 |
| 0.2776 | 13.99 | 1170 | 0.1872 |
| 0.2269 | 14.89 | 1245 | 0.1868 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "csebuetnlp/banglat5", "model-index": [{"name": "bn_news_article_summarization", "results": []}]} | fahad1770/bengali_news_article_summarization_BanglaT5 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/banglat5",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:11:01+00:00 |
null | null | {"license": "mit"} | stephenakwaowo/2023-mlb-radar-charts | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T22:11:35+00:00 |
|
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Max87152/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | Max87152/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-05-02T22:12:14+00:00 |
text2text-generation | transformers | {} | samzirbo/mt5.baseline.5e-3 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:13:09+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. -->
# llava_13b_exact_gps_coordinates
This model is a fine-tuned version of [liuhaotian/llava-v1.5-13b](https://huggingface.co/liuhaotian/llava-v1.5-13b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- 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: 0.03
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8953 | 1.0 | 9 | 1.0184 |
| 0.2243 | 2.0 | 18 | 1.1205 |
| 0.0751 | 3.0 | 27 | 1.1985 |
| 0.0279 | 4.0 | 36 | 1.3922 |
| 0.0093 | 5.0 | 45 | 1.5086 |
| 0.0114 | 6.0 | 54 | 1.4457 |
| 0.003 | 7.0 | 63 | 1.4304 |
| 0.0028 | 8.0 | 72 | 1.4950 |
| 0.0011 | 9.0 | 81 | 1.5275 |
| 0.0011 | 10.0 | 90 | 1.5826 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Tokenizers 0.15.1 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "liuhaotian/llava-v1.5-13b", "model-index": [{"name": "llava_13b_exact_gps_coordinates", "results": []}]} | emendes3/llava_13b_exact_gps_coordinates | null | [
"peft",
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:liuhaotian/llava-v1.5-13b",
"4-bit",
"region:us"
] | null | 2024-05-02T22:13:11+00:00 |
text-generation | transformers | {"license": "mit"} | coming-san-fran/baka | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:14:31+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-410m_mz-133_EnronSpam_n-its-10-seed-4
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) 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-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-133_EnronSpam_n-its-10-seed-4", "results": []}]} | AlignmentResearch/robust_llm_pythia-410m_mz-133_EnronSpam_n-its-10-seed-4 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:14:58+00:00 |
text-generation | transformers | Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement:
https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
This is by far the most completely uncensored Llama 3 8b instruct model. It will literally never refuse anything.
So as a reminder, with great power comes great responsibility.
In terms of reasoning and intelligence, this model is probably worse than the OG model because of the decensoring. However, if you have issues with refusals then this will be superior just because it will not refuse.
Will soon have quants uploaded here on HF and have it up on our site https://awanllm.com for anyone to try.
OpenLLM Benchmark:
Training:
- 4096 sequence length, while the base model is 8192 sequence length. From testing it still performs the same 8192 context just fine.
- Training duration is around 3 days on an RTX 4090, using 4-bit loading and Qlora 64-rank 128-alpha resulting in ~2% trainable weights.
- Added DPO fine tuning aside from a more curated dataset for this v0.2 model.
Instruct format:
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
Quants:
FP16: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Cumulus-v0.2
GGUF: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Cumulus-v0.2-GGUF
| {"license": "llama3"} | AwanLLM/Awanllm-Llama-3-8B-Cumulus-v0.2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:16:36+00:00 |
null | null | {} | fqb/elm-v0.1_news_summarization-GGUF | null | [
"region:us"
] | null | 2024-05-02T22:17:28+00:00 |
|
null | null | {} | nes470/quiz-bowl-model | null | [
"region:us"
] | null | 2024-05-02T22:17:30+00:00 |
|
automatic-speech-recognition | transformers | {} | nextera/whisper-small-fa-75-64-1e-05 | null | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T22:19:59+00:00 |
|
text-generation | transformers | # Model Card for Mixtral-8x7B
The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mistral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
## Warning
This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
## Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
### In half-precision
Note `float16` precision only works on GPU devices
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
### Load the model with Flash Attention 2
<details>
<summary> Click to expand </summary>
```diff
+ import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mistralai/Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True)
text = "Hello my name is"
+ inputs = tokenizer(text, return_tensors="pt").to(0)
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
</details>
## Notice
Mixtral-8x7B is a pretrained base model and therefore does not have any moderation mechanisms.
# The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | {"language": ["fr", "it", "de", "es", "en"], "license": "apache-2.0", "tags": ["moe"]} | pcmoritz/Mixtral-8x7B-v0.1-fp8-act-scale | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"fr",
"it",
"de",
"es",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:20:10+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. -->
# llama2-poison-10p-0502
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 844 | nan |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama2-poison-10p-0502", "results": []}]} | Jackie999/llama2-poison-10p-0502 | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-05-02T22:20:30+00:00 |
text-generation | transformers |
# Zhongli DialoGPT Model
| {"license": "cc", "tags": ["conversational"]} | javamaster44/DialoGPT-Medium-Zhongli | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"license:cc",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:20:49+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/p68hzmi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T22:21:57+00:00 |
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