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LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2 | LoneStriker | 2023-11-30T01:11:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:11:45Z | 2023-11-30T00:41:52.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
### Quantized models
* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
## Results
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536 | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2 | [
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Rbanerjee/roberta_qa_model_with_gnn | Rbanerjee | 2023-11-30T00:42:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:42:02Z | 2023-11-30T00:42:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Rbanerjee/roberta_qa_model_with_gnn | [
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vikp/pdf_postprocessor_t5 | vikp | 2023-11-30T00:44:59Z | 0 | 0 | null | [
"transformers",
"pytorch",
"t5",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:44:59Z | 2023-11-30T00:44:29.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | vikp/pdf_postprocessor_t5 | [
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rodrigomokipo/bowie3 | rodrigomokipo | 2023-11-30T00:45:46Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:45:46Z | 2023-11-30T00:45:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rodrigomokipo/bowie3 | [
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zibajoon/20231129_Bart-lg-samsum_10ep_Summ | zibajoon | 2023-11-30T00:46:17Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:linydub/bart-large-samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:46:17Z | 2023-11-30T00:45:35.000Z | null | null | Temporary Redirect. Redirecting to /zibajoon/20231129_Bart-Lg-samsum_8ep_Summ_Loss_1.0_R1_0.52/resolve/main/README.md | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | zibajoon/20231129_Bart-lg-samsum_10ep_Summ | [
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Quasar-Kim/kcformer-3b | Quasar-Kim | 2023-11-30T01:18:22Z | 0 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:18:22Z | 2023-11-30T00:46:54.000Z | null | null | ---
license: mit
---
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Quasar-Kim/kcformer-3b | [
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LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2 | LoneStriker | 2023-11-30T01:11:50Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:11:50Z | 2023-11-30T00:48:07.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
### Quantized models
* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
## Results
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536 | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2 | [
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GoatMilk98/635-a2-ibert | GoatMilk98 | 2023-11-30T00:50:12Z | 0 | 0 | null | [
"transformers",
"pytorch",
"ibert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:50:12Z | 2023-11-30T00:49:26.000Z | null | null | Entry not found | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | GoatMilk98/635-a2-ibert | [
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imdltjwns/whisper-small-v1 | imdltjwns | 2023-11-30T00:49:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:49:26Z | 2023-11-30T00:49:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | imdltjwns/whisper-small-v1 | [
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mestikon/mest | mestikon | 2023-11-30T00:50:57Z | 0 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/sdxl-turbo",
"region:us",
"has_space"
] | 2023-11-30T00:50:57Z | 2023-11-30T00:50:43.000Z | null | null | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/02-15-13-4HRE9NIATBvfQAAAAASUVORK5CYII=.jpg
base_model: stabilityai/sdxl-turbo
instance_prompt: mestikon
---
# Mestikon
<Gallery />
## Trigger words
You should use `mestikon` to trigger the image generation.
## Download model
[Download](/mestikon/mest/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | mestikon/mest | [
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError60percentSeed100 | behzadnet | 2023-11-30T00:51:49Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-30T00:51:49Z | 2023-11-30T00:51:42.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Environmental Impact
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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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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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TheBloke/LLaMA2-13B-Psyfighter2-GPTQ | TheBloke | 2023-11-30T00:51:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:51:53Z | 2023-11-30T00:51:52.000Z | null | null | ---
base_model: KoboldAI/LLaMA2-13B-Psyfighter2
inference: false
license: llama2
model_creator: KoboldAI
model_name: Llama2 13B Psyfighter2
model_type: llama
prompt_template: "### Instruction: \n{prompt}\n### Response:\n"
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Llama2 13B Psyfighter2 - GPTQ
- Model creator: [KoboldAI](https://huggingface.co/KoboldAI)
- Original model: [Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2)
<!-- description start -->
# Description
This repo contains GPTQ model files for [KoboldAI's Llama2 13B Psyfighter2](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GGUF)
* [KoboldAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KoboldAI/LLaMA2-13B-Psyfighter2)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca-Tiefighter
```
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/LLaMA2-13B-Psyfighter2-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/LLaMA2-13B-Psyfighter2-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `LLaMA2-13B-Psyfighter2-GPTQ`:
```shell
mkdir LLaMA2-13B-Psyfighter2-GPTQ
huggingface-cli download TheBloke/LLaMA2-13B-Psyfighter2-GPTQ --local-dir LLaMA2-13B-Psyfighter2-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir LLaMA2-13B-Psyfighter2-GPTQ
huggingface-cli download TheBloke/LLaMA2-13B-Psyfighter2-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir LLaMA2-13B-Psyfighter2-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir LLaMA2-13B-Psyfighter2-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LLaMA2-13B-Psyfighter2-GPTQ --local-dir LLaMA2-13B-Psyfighter2-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/LLaMA2-13B-Psyfighter2-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/LLaMA2-13B-Psyfighter2-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/LLaMA2-13B-Psyfighter2-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `LLaMA2-13B-Psyfighter2-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/LLaMA2-13B-Psyfighter2-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/LLaMA2-13B-Psyfighter2-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: KoboldAI's Llama2 13B Psyfighter2
# LLAMA2-13B-Psyfighter2
Psyfighter is a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows and was made possible thanks to the KoboldAI merge request service.
The intent was to add medical data to supplement the models fictional ability with more details on anatomy and mental states. Due to the low ratio's of medical data and the high ratio's of fiction this model should not be used for medical advice or therapy because of its high chance of pulling in fictional data.
The following mergekit recipe was used:
```
merge_method: task_arithmetic
base_model: TheBloke/Llama-2-13B-fp16
models:
- model: TheBloke/Llama-2-13B-fp16
- model: KoboldAI/LLaMA2-13B-Tiefighter
parameters:
weight: 1.0
- model: Doctor-Shotgun/cat-v1.0-13b
parameters:
weight: 0.01
- model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
parameters:
weight: 0.02
dtype: float16
```
*V1 of this model was published under the account of the creator of the merge
This model contains the following ingredients from their upstream models for as far as we can track them:
- KoboldAI/LLaMA2-13B-Tiefighter
- Undi95/Xwin-MLewd-13B-V0.2
- - Undi95/ReMM-S-Light
- Undi95/CreativeEngine
- Brouz/Slerpeno
- - elinas/chronos-13b-v2
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2
- CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2
- - KoboldAI/LLAMA2-13B-Holodeck-1
- NousResearch/Nous-Hermes-13b
- OpenAssistant/llama2-13b-orca-8k-3319
- ehartford/WizardLM-1.0-Uncensored-Llama2-13b
- Henk717/spring-dragon
- The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible)
- - SuperCOT (Undisclosed version)
- elinas/chronos-13b-v2 (Version assumed)
- NousResearch/Nous-Hermes-Llama2-13b
- stabilityai/StableBeluga-13B (Version assumed)
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/Storytelling-v1-13B-lora
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
- - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp"
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
- PocketDoc/Dans-RetroRodeo-13b
- Blackroot/Llama-2-13B-Storywriter-LORA
- Doctor-Shotgun/cat-v1.0-13b
- Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
- meta-llama/Llama-2-13b-chat-hf
- lemonilia/limarp-llama2-v2
While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible!
Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.
# Usage
This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.
## Story Writing
Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.
### Generate a story on demand
To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.
## Chatbots and persona's
This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further.
For example, you can put this in memory in regular chat mode:
```
### Instruction:
Generate a conversation between Alice and Jeb where they discuss language models.
In this conversation Henk is excited to teach Alice about Psyfighter.
### Response:
```
Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.
## Instruct Prompting
This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.
During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.
Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.
## Adventuring and Adventure Games
This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).
It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.
## Discovered something cool and want to engage with us?
Join our community at https://koboldai.org/discord !
We can also provide assistance in making your own merges.
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] |
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError60percentSeed100 | behzadnet | 2023-11-30T00:51:59Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-30T00:51:59Z | 2023-11-30T00:51:56.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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## Environmental Impact
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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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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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mncai/Pr_Mistral_7B-Sh5k_Wi5k_Ne5k_Ct5k-Lr05_no-sys_Ep4 | mncai | 2023-11-30T00:56:20Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:56:20Z | 2023-11-30T00:52:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | mncai/Pr_Mistral_7B-Sh5k_Wi5k_Ne5k_Ct5k-Lr05_no-sys_Ep4 | [
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CitrusBoy/demo-finetuned-model-DnD | CitrusBoy | 2023-11-30T00:54:06Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:54:06Z | 2023-11-30T00:52:20.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | CitrusBoy/demo-finetuned-model-DnD | [
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muthureal/testmodel | muthureal | 2023-11-30T00:53:34Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-30T00:53:34Z | 2023-11-30T00:52:31.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | muthureal/testmodel | [
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GraydientPlatformAPI/juggernaut7-xl | GraydientPlatformAPI | 2023-11-30T01:03:41Z | 0 | 0 | null | [
"diffusers",
"license:openrail",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-30T01:03:41Z | 2023-11-30T00:53:05.000Z | null | null | ---
license: openrail
---
| null | diffusers | null | null | null | null | null | null | null | null | null | null | GraydientPlatformAPI/juggernaut7-xl | [
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oliver27/nuevo | oliver27 | 2023-11-30T00:53:11Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-30T00:53:11Z | 2023-11-30T00:53:10.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | oliver27/nuevo | [
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joyfine/vicuna-7b-fine-tuning_twitter_20_128 | joyfine | 2023-11-30T00:59:33Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:59:33Z | 2023-11-30T00:54:03.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | joyfine/vicuna-7b-fine-tuning_twitter_20_128 | [
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LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2 | LoneStriker | 2023-11-30T01:11:56Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:11:56Z | 2023-11-30T00:54:29.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
### Quantized models
* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
## Results
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536 | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2 | [
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oscorrea/scores-flan-t5-large-29-11 | oscorrea | 2023-11-30T01:07:32Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:07:32Z | 2023-11-30T00:55:10.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | oscorrea/scores-flan-t5-large-29-11 | [
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hrysto97/ppo-LunarLander-v2.8 | hrysto97 | 2023-11-30T01:11:26Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-30T01:11:26Z | 2023-11-30T00:56:08.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 290.83 +/- 12.35
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | hrysto97/ppo-LunarLander-v2.8 | [
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moieric11/RVCModels | moieric11 | 2023-11-30T00:58:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:58:40Z | 2023-11-30T00:56:46.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | moieric11/RVCModels | [
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wnic00/distilbert-new2 | wnic00 | 2023-11-30T00:58:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:58:27Z | 2023-11-30T00:58:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | wnic00/distilbert-new2 | [
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squeeze-ai-lab/sq-mistral-7b-instruct-w3 | squeeze-ai-lab | 2023-11-30T01:00:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:00:42Z | 2023-11-30T01:00:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | squeeze-ai-lab/sq-mistral-7b-instruct-w3 | [
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LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2 | LoneStriker | 2023-11-30T01:12:00Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:12:00Z | 2023-11-30T01:00:45.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
### Quantized models
* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
## Results
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536 | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2 | [
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Apinapi/DatasetLucas | Apinapi | 2023-11-30T01:01:23Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-30T01:01:23Z | 2023-11-30T01:00:56.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Apinapi/DatasetLucas | [
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squeeze-ai-lab/sq-mistral-7b-instruct-w4 | squeeze-ai-lab | 2023-11-30T01:00:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:00:57Z | 2023-11-30T01:00:57.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | squeeze-ai-lab/sq-mistral-7b-instruct-w4 | [
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FluffyKaeloky/Euryale-Inverted-L2-70B-exl2-6.0bpw | FluffyKaeloky | 2023-11-30T01:14:38Z | 0 | 0 | null | [
"ExLlamaV2",
"text-generation-inference",
"llama",
"text-generation",
"en",
"region:us"
] | 2023-11-30T01:14:38Z | 2023-11-30T01:06:35.000Z | null | null | ---
language:
- en
pipeline_tag: text-generation
tags:
- ExLlamaV2
- text-generation-inference
- llama
---
Exl2 version of Sao10K's Euryale-Inverted-L2-70B.
6.0 bpw version.
Model original card : https://huggingface.co/Sao10K/Euryale-Inverted-L2-70B
Tested with 16K context with alpha value, fits just below 60Gb of VRAM divided over 3 GPUs. | null | null | text-generation | null | null | null | null | null | null | null | null | null | FluffyKaeloky/Euryale-Inverted-L2-70B-exl2-6.0bpw | [
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LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2 | LoneStriker | 2023-11-30T01:12:05Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:12:05Z | 2023-11-30T01:07:10.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B
NeuralHermes is an [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset. It surpasses the original model on several benchmarks (see results).
It is directly inspired by the RLHF process described by [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
### Quantized models
* GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
* AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
* EXL2 (5pbw): https://huggingface.co/IconicAI/NeuralHermes-2.5-Mistral-7B-exl2-5bpw
## Results
Teknium (author of OpenHermes-2.5-Mistral-7B) benchmarked the model ([see his tweet](https://twitter.com/Teknium1/status/1729955709377503660)).
Results are improved on every benchmark: **AGIEval** (from 43.07% to 43.62%), **GPT4All** (from 73.12% to 73.25%), and **TruthfulQA**.
### AGIEval
### GPT4All
### TruthfulQA
You can check the Weights & Biases project [here](https://wandb.ai/mlabonne/NeuralHermes-2-5-Mistral-7B/overview?workspace=user-mlabonne).
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16
* lora_alpha=16
* lora_dropout=0.05
* bias="none"
* task_type="CAUSAL_LM"
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4
* gradient_accumulation_steps=4
* gradient_checkpointing=True
* learning_rate=5e-5
* lr_scheduler_type="cosine"
* max_steps=200
* optim="paged_adamw_32bit"
* warmup_steps=100
**DPOTrainer**:
* beta=0.1
* max_prompt_length=1024
* max_length=1536 | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2 | [
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igorvln/dare_gpt2_semeval2018_byrelation_finetuning_with_rl | igorvln | 2023-11-30T01:11:14Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:11:14Z | 2023-11-30T01:08:26.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | igorvln/dare_gpt2_semeval2018_byrelation_finetuning_with_rl | [
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alfredolozano/CODEX_LoRA2 | alfredolozano | 2023-11-30T01:10:02Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"if",
"if-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-30T01:10:02Z | 2023-11-30T01:09:43.000Z | null | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: <class 'str'>
tags:
- if
- if-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - alfredolozano/CODEX_LoRA2
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <class 'str'> using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | alfredolozano/CODEX_LoRA2 | [
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Broomva/t5-base-translation-spa-guc | Broomva | 2023-11-30T01:21:51Z | 0 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:21:51Z | 2023-11-30T01:10:30.000Z | null | null | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: t5-base-translation-spa-guc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-translation-spa-guc
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) 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: 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
- lr_scheduler_warmup_steps: 10
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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01GangaPutraBheeshma/colab_text_generation_FT_opt_on_dolly_UT_V2 | 01GangaPutraBheeshma | 2023-11-30T01:12:09Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:12:09Z | 2023-11-30T01:11:19.000Z | null | null | ---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: colab_text_generation_FT_opt_on_dolly_UT_V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# colab_text_generation_FT_opt_on_dolly_UT_V2
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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esmarquez17/hate-social-network | esmarquez17 | 2023-11-30T01:22:28Z | 0 | 0 | null | [
"transformers",
"tf",
"roberta",
"text-classification",
"generated_from_keras_callback",
"base_model:esmarquez17/fine-tunning-roberta-bne-hate-offensive",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:22:28Z | 2023-11-30T01:14:35.000Z | null | null | ---
license: apache-2.0
base_model: esmarquez17/fine-tunning-roberta-bne-hate-offensive
tags:
- generated_from_keras_callback
model-index:
- name: hate-social-network
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# hate-social-network
This model is a fine-tuned version of [esmarquez17/fine-tunning-roberta-bne-hate-offensive](https://huggingface.co/esmarquez17/fine-tunning-roberta-bne-hate-offensive) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 3050, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | esmarquez17/hate-social-network | [
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karawalla/mistral_b_karawalla_new2 | karawalla | 2023-11-30T01:15:17Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"region:us"
] | 2023-11-30T01:15:17Z | 2023-11-30T01:15:12.000Z | null | null | ---
library_name: peft
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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#### Preprocessing [optional]
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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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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | karawalla/mistral_b_karawalla_new2 | [
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Kobee/Llama2_7B_chat_meetingBank | Kobee | 2023-11-30T01:16:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:16:53Z | 2023-11-30T01:16:53.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Kobee/Llama2_7B_chat_meetingBank | [
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mdubeau19/thematic_analysis | mdubeau19 | 2023-11-30T01:21:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:21:10Z | 2023-11-30T01:21:10.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mdubeau19/thematic_analysis | [
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awilliamson/gimli_math | awilliamson | 2023-11-30T01:26:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:26:59Z | 2023-11-30T01:21:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | awilliamson/gimli_math | [
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younanna/llama2-7b_orca_mink_perc_average_top_10000 | younanna | 2023-11-30T01:22:04Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:22:04Z | 2023-11-30T01:22:04.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | younanna/llama2-7b_orca_mink_perc_average_top_10000 | [
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nairaxo/swahili-text-summarization | nairaxo | 2023-11-30T01:24:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:24:01Z | 2023-11-30T01:24:01.000Z | null | null | Invalid username or password. | null | null | null | null | null | null | null | null | null | null | null | null | nairaxo/swahili-text-summarization | [
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AhmedEladl/my_awesome_swag_model | AhmedEladl | 2023-11-30T01:25:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:25:52Z | 2023-11-30T01:25:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AhmedEladl/my_awesome_swag_model | [
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Weni/Test4-ZeroShot-Multilanguage-3.0-Llama2-13B | Weni | 2023-11-30T01:26:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:26:39Z | 2023-11-30T01:26:39.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Weni/Test4-ZeroShot-Multilanguage-3.0-Llama2-13B | [
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