id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
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LoneStriker/deepseek-llm-67b-chat-4.0bpw-h6-exl2 | LoneStriker | 2023-11-30T00:09:18Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:09:18Z | 2023-11-29T20:43:50.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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GR4V311/jdmangas2 | GR4V311 | 2023-11-29T20:51:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:51:24Z | 2023-11-29T20:51:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | GR4V311/jdmangas2 | [
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Blessing988/videomae-base-finetuned-ucf101-subset_500_epochs | Blessing988 | 2023-11-29T21:57:15Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:57:15Z | 2023-11-29T20:51:45.000Z | null | null | Entry not found | null | transformers | video-classification | null | null | null | null | null | null | null | null | null | Blessing988/videomae-base-finetuned-ucf101-subset_500_epochs | [
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AyyCompadre/Hugging_Face_Certification_Project | AyyCompadre | 2023-11-29T20:53:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:53:54Z | 2023-11-29T20:53:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AyyCompadre/Hugging_Face_Certification_Project | [
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staticai/Zro1 | staticai | 2023-11-29T22:07:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:07:24Z | 2023-11-29T20:54:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | staticai/Zro1 | [
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gms0817/mistral-ehrs-transcription-summary-2500 | gms0817 | 2023-11-29T20:55:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T20:55:59Z | 2023-11-29T20:55:59.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | gms0817/mistral-ehrs-transcription-summary-2500 | [
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Madserker/Courtesy-Dizart | Madserker | 2023-11-29T21:03:09Z | 0 | 0 | null | [
"license:mpl-2.0",
"region:us"
] | 2023-11-29T21:03:09Z | 2023-11-29T20:57:45.000Z | null | null | ---
license: mpl-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Madserker/Courtesy-Dizart | [
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lesliegar1123/Leslie | lesliegar1123 | 2023-11-29T20:58:36Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T20:58:36Z | 2023-11-29T20:58:35.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | lesliegar1123/Leslie | [
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Frixi/CAS_Lead_Singer | Frixi | 2023-11-29T21:01:05Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T21:01:05Z | 2023-11-29T21:00:45.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Frixi/CAS_Lead_Singer | [
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Nikt271/Wojti2000 | Nikt271 | 2023-11-29T21:03:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:03:42Z | 2023-11-29T21:01:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Nikt271/Wojti2000 | [
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Jaspernl/distil-whisper-small-nl-ft | Jaspernl | 2023-11-29T23:47:20Z | 0 | 0 | null | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:47:20Z | 2023-11-29T21:03:14.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Jaspernl/distil-whisper-small-nl-ft | [
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VitacapsDetox/VitacapsDetox | VitacapsDetox | 2023-11-29T21:04:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:04:43Z | 2023-11-29T21:03:35.000Z | null | null | Vitacaps Detox Mexico: cápsula, Precio, Opiniones, reseñas, Ingredientes, Píldora, Beneficios, costo!
Vitacaps Detox es un suplemento de desintoxicación de última generación destinado a atacar y eliminar los parásitos dañinos que pueden vivir dentro del cuerpo humano. Vitacaps Detox Mexico
Vitacaps Detox ¡¡Compra ahora!! ¡Haga clic en el enlace a continuación para obtener más información y obtenga un 50% de descuento ahora! apresúrate !!
leer más: https://www.nutritioncrawler.com/VitacDetoMexi
https://sites.google.com/view/vitacaps-detox/home
¿Qué es Vitacaps Detox?
Los parásitos, cuyo efecto frecuentemente se subestima, pueden provocar una variedad de problemas médicos, desde problemas estomacales hasta cansancio. Vitacaps Detox cápsula está elaborado con una mezcla de ingredientes naturales conocidos por sus propiedades antiparasitarias, que brindan una solución segura y convincente para limpiar el cuerpo.
https://www.nutritioncrawler.com/VitacDetoMexi
https://sites.google.com/view/vitacaps-detox/home
https://healthtoned.blogspot.com/2023/11/vitacaps-detox.html
https://www.q8101.com/bb/23933/vitacaps-detox-mexico
https://www.dibiz.com/vitacapsdetox
https://www.weddingwire.com/website/vitacaps-and-detox
https://infogram.com/vitacaps-detox-mexico-1h0r6rpje97lw2e?live
https://softisenilspain.hashnode.dev/vitacaps-detox-mexico
https://vitacapsdetox.company.site/
https://sway.office.com/auCfgoQCsqbMANcl
https://gamma.app/docs/Vitacaps-Detox-Mexico-hv657uu19l9suv9?mode=doc
https://healthytalk24x7.wixsite.com/sunshine/post/vitacaps-detox-mexico
https://groups.google.com/g/snshine/c/1S_GzBmTff0
https://www.forexagone.com/forum/presentation-traders/vitacaps-detox-mexico-91683#189155
https://devfolio.co/projects/vitacaps-detox-mexico-6f62
https://replit.com/@vitacapsdetox
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mdgossett/sd_1-5_output_30_images_batch_size_16_V2 | mdgossett | 2023-11-29T21:06:39Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:06:39Z | 2023-11-29T21:06:39.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mdgossett/sd_1-5_output_30_images_batch_size_16_V2 | [
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matthh/Mistral-7B-v0.1_poetry-fine-tuned | matthh | 2023-11-29T21:07:05Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | 2023-11-29T21:07:05Z | 2023-11-29T21:06:56.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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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: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
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bachor2/model1 | bachor2 | 2023-11-29T21:42:47Z | 0 | 0 | null | [
"tensorboard",
"license:apache-2.0",
"region:us"
] | 2023-11-29T21:42:47Z | 2023-11-29T21:06:58.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | bachor2/model1 | [
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Samoed/llama-2-7b-airplane-tickets-gguf | Samoed | 2023-11-29T21:09:52Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T21:09:52Z | 2023-11-29T21:07:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Samoed/llama-2-7b-airplane-tickets-gguf | [
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appvoid/palmer-001-v0.1 | appvoid | 2023-11-29T21:23:50Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:appvoid/no-prompt-5k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:23:50Z | 2023-11-29T21:07:48.000Z | null | null | ---
license: apache-2.0
datasets:
- appvoid/no-prompt-5k
---
| **QLoRA parameters** | |
|---------------------------|---------------------------------------------|
| LoRA attention dimension | lora_r = 64 |
| Alpha parameter for LoRA scaling | lora_alpha = 16 |
| Dropout probability for LoRA layers | lora_dropout = 0.1 |
| **bitsandbytes parameters** | |
|---------------------------|---------------------------------------------|
| Activate 4-bit precision base model loading | use_4bit = True |
| Compute dtype for 4-bit base models | bnb_4bit_compute_dtype = "float16" |
| Quantization type (fp4 or nf4) | bnb_4bit_quant_type = "nf4" |
| Activate nested quantization for 4-bit base models (double quantization) | use_nested_quant = False |
| **TrainingArguments parameters** | |
|-------------------------------|---------------------------------------------|
| Output directory where the model predictions and checkpoints will be stored | output_dir = "./results" |
| Number of training epochs | num_train_epochs = 2 |
| Enable fp16/bf16 training (set bf16 to True with an A100) | fp16 = False, bf16 = False |
| Batch size per GPU for training | per_device_train_batch_size = 8 |
| Batch size per GPU for evaluation | per_device_eval_batch_size = 8 |
| Number of update steps to accumulate the gradients for | gradient_accumulation_steps = 4 |
| Enable gradient checkpointing | gradient_checkpointing = True |
| Maximum gradient normal (gradient clipping) | max_grad_norm = 0.3 |
| Initial learning rate (AdamW optimizer) | learning_rate = 2e-4 |
| Weight decay to apply to all layers except bias/LayerNorm weights | weight_decay = 0.001 |
| Optimizer to use | optim = "paged_adamw_32bit" |
| Learning rate schedule | lr_scheduler_type = "cosine" |
| Number of training steps (overrides num_train_epochs) | max_steps = -1 |
| Ratio of steps for a linear warmup (from 0 to learning rate) | warmup_ratio = 0.03 |
| Group sequences into batches with the same length | group_by_length = True |
| Save checkpoint every X updates steps | save_steps = 0 |
| Log every X updates steps | logging_steps = 10 |
| **SFT parameters** | |
|-----------------------------|---------------------------------------------|
| Maximum sequence length to use | max_seq_length = None |
| Pack multiple short examples in the same input sequence to increase efficiency | packing = False |
| Load the entire model on GPU 0 | device_map = {"": 0} |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | appvoid/palmer-001-v0.1 | [
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chrisgru/llama-7b-v0 | chrisgru | 2023-11-29T21:10:11Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:10:11Z | 2023-11-29T21:09:34.000Z | null | null | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: model_output
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# model_output
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6505
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9209 | 0.11 | 20 | 0.8467 |
| 0.767 | 0.22 | 40 | 0.7407 |
| 0.725 | 0.33 | 60 | 0.7076 |
| 0.689 | 0.44 | 80 | 0.6931 |
| 0.7331 | 0.55 | 100 | 0.6865 |
| 0.6744 | 0.66 | 120 | 0.6761 |
| 0.7059 | 0.77 | 140 | 0.6671 |
| 0.6711 | 0.87 | 160 | 0.6692 |
| 0.6975 | 0.98 | 180 | 0.6604 |
| 0.6039 | 1.09 | 200 | 0.6641 |
| 0.6087 | 1.2 | 220 | 0.6574 |
| 0.6164 | 1.31 | 240 | 0.6583 |
| 0.6132 | 1.42 | 260 | 0.6604 |
| 0.6136 | 1.53 | 280 | 0.6580 |
| 0.6485 | 1.64 | 300 | 0.6571 |
| 0.6205 | 1.75 | 320 | 0.6560 |
| 0.5973 | 1.86 | 340 | 0.6514 |
| 0.6048 | 1.97 | 360 | 0.6505 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | chrisgru/llama-7b-v0 | [
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pranchana/meduza | pranchana | 2023-11-29T21:15:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:15:24Z | 2023-11-29T21:09:39.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pranchana/meduza | [
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Alphonsce/Avrora-lora | Alphonsce | 2023-11-29T21:10:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:10:29Z | 2023-11-29T21:10:29.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Alphonsce/Avrora-lora | [
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chrisgru/llama-7b-v1 | chrisgru | 2023-11-29T21:11:22Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:11:22Z | 2023-11-29T21:10:38.000Z | null | null | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: model_output
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. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# model_output
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6558
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8594 | 0.11 | 20 | 0.8079 |
| 0.7572 | 0.22 | 40 | 0.7334 |
| 0.7202 | 0.33 | 60 | 0.7059 |
| 0.6849 | 0.44 | 80 | 0.6899 |
| 0.7245 | 0.55 | 100 | 0.6865 |
| 0.6738 | 0.66 | 120 | 0.6752 |
| 0.703 | 0.77 | 140 | 0.6705 |
| 0.6725 | 0.87 | 160 | 0.6702 |
| 0.6976 | 0.98 | 180 | 0.6617 |
| 0.6018 | 1.09 | 200 | 0.6663 |
| 0.6093 | 1.2 | 220 | 0.6593 |
| 0.6167 | 1.31 | 240 | 0.6611 |
| 0.6128 | 1.42 | 260 | 0.6611 |
| 0.6098 | 1.53 | 280 | 0.6590 |
| 0.6463 | 1.64 | 300 | 0.6568 |
| 0.6174 | 1.75 | 320 | 0.6534 |
| 0.5943 | 1.86 | 340 | 0.6519 |
| 0.602 | 1.97 | 360 | 0.6516 |
| 0.5815 | 2.08 | 380 | 0.6540 |
| 0.5496 | 2.19 | 400 | 0.6576 |
| 0.5597 | 2.3 | 420 | 0.6609 |
| 0.5433 | 2.4 | 440 | 0.6691 |
| 0.5498 | 2.51 | 460 | 0.6742 |
| 0.5418 | 2.62 | 480 | 0.6684 |
| 0.5337 | 2.73 | 500 | 0.6672 |
| 0.5045 | 2.84 | 520 | 0.6553 |
| 0.5659 | 2.95 | 540 | 0.6558 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | chrisgru/llama-7b-v1 | [
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pranchana/medz | pranchana | 2023-11-29T21:32:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:32:57Z | 2023-11-29T21:11:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pranchana/medz | [
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kzm16/Llama-2-7b-chat-finetune | kzm16 | 2023-11-29T21:12:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:12:19Z | 2023-11-29T21:12:19.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kzm16/Llama-2-7b-chat-finetune | [
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docketanalyzer/docket-lm | docketanalyzer | 2023-11-29T21:14:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:14:03Z | 2023-11-29T21:14:03.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | docketanalyzer/docket-lm | [
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toninhodjj/flakespower | toninhodjj | 2023-11-29T21:14:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:14:26Z | 2023-11-29T21:14:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | toninhodjj/flakespower | [
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Taoshi/model_v4 | Taoshi | 2023-11-29T21:58:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:58:27Z | 2023-11-29T21:14:37.000Z | null | null | ```text
---
Copyright (c) 2023 Taoshi Inc
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
---
```
# Background
```text
The models provided here were created using open source modeling techniques
provided in https://github.com/taoshidev/time-series-prediction-subnet (TSPS).
They were achieved using the runnable/miner_training.py, and tested against
existing models and dummy models in runnable/miner_testing.py.
```
# Build Strategy
```text
This section outlines the strategy used to build the models.
```
## Understanding Dataset Used
```text
The dataset used to build the models can be generated using the
runnable/generate_historical_data.py. A lookback period between June 2022 and
July 2023 on the 5m interval was used to train the model. Through analysis, the
reason this dataset was used is because historical data beyond June 2022 provides
strongly trending price movement or data movement that is from a period where
Bitcoin's market cap was too small to be relevant to where Bitcoin is now.
Therefore, using more recent data was used which correlates to the current market
cap and macroeconomic conditions where its uncertain we'll continue to get highly
trending Bitcoin data.
Testing data was used between June 2023 and Nov 2023 to determine performance of
the models. This was tested using the runnable/miner_testing.py file with a
separately generated test dataset from runnable/generate_historical_data.py.
```
## Understanding Model Creation
```text
As of now, the TSPS infrastructure only provides close, high, low, and volume. It
also provides financial indicators such as RSI, MACD, and Bollinger Bands but they
were not used for the purposes of training these models.
The models were derived using a variety of windows and iterations through the June
2022 to June 2023 dataset. The strategy to derive the model was the following:
base_mining_model = BaseMiningModel(len(prep_dataset.T)) \
.set_neurons([[1024, 0]]) \
.set_window_size(100) \
.set_learning_rate(0.0000001) \
.set_batch_size(500) \
.set_model_dir(f'mining_models/model1.h5')
base_mining_model.train(prep_dataset, epochs=25)
where an LSTM model was created by using a few or no stacked layers. Most of the
v4 models are actually not stacked as they performed better not being stacked for
the most part. This could very likely change as more feature inputs are added (this
is being worked on as part of the open source infra in TSPS). The window size of
100 helped best predict the outcome, derived in mining_objects/base_mining_model.py
```
## Understanding Training Decisions
```text
Training the model used the previous 601 rows of data as an input. This is because
500 rows were used to batch, and we are looking to predict 100 rows into the future
(the challenge presented in the Time Series Prediction Subnet). Measures were taken
to ensure all data was trained on in the training data.
Each set of 601 rows was trained on 25 times, inside another loop which iterated on
the entirety of the dataset from 6/22 to 6/23 50 times. This provided the model the
ability to get granular with details yet not overfit to any single set of rows at
once. Therefore, a multi-layered looping infrastructure was used to derive the models.
for x in range(50):
for i in range(25):
train_model()
```
## Strategy to Predict
```text
The strategy to predict 100 closes of data into the future was to use a 1 step
methodology of predicting 1 step at 100 intervals into the future and connect the
information by generating a line from the last close to the prediction 100 closes
into the future. By doing so, the model could learn to predict a single step rather
than all 100 where loss could continue to increase with each misstep.
```
# Model V5
```text
Here's the text spaced out for readability in a README file:
Recommendations on how to perform better than V4 and what Model V5 will look like
are outlined below:
1. Concentrate on more difficult moves
2. Get more granular data (1m)
3. Get more data sources
4. Use more predicted steps
-- Concentrate on more difficult moves
The Time Series Prediction Subnet will reward models that are capable of predicting
more "difficult" movements in the market more than those that are less difficult.
Therefore, taking a strategy to train your model on larger movements or bigger
magnitude movements would be a good consideration. Some additional details on how
difficulty is calculated will be released soon but it is a combination of the
magnitude of the movement with the std dev of the movement in the predicted interval.
-- Get more granular data (1m)
With these larger magnitude movements, a strategy to get more granular with the data
would be recommended. Using 1m data to train rather than 5m would help the models
better predict information.
-- Get more data sources
Beyond using financial market indicators like RSI, MACD, and Bollinger Bands, the
TSPS open source infra will gather information for miners to help train.
The TSPS infrastructure will be adding data scrapers and using those data scrapers
to automatically gather information for you. The following pieces of information will
be gathered & accessible through the open source infra:
- Bitcoin open interest
- Bitcoin OHLCV data
- Bitcoin funding rate
- DXY OHLCV data
- Gold OHLCV data
- S&P 500 OHLCV data
- Bitcoin dominance
- Historical news data (sentiment analysis)
Using this information will provide models with information they can use to better
predict prices as markets correlate in movement and Bitcoin responds to other markets.
-- Use more predicted steps
Rather than only predicting a single step at the 100th predicted close in the future,
predict more steps. This can be achieved by training multiple models, for example,
10 models each at 10 closes into the future (10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
or by using a multi-step model with 10 steps. Both will achieve more granularity when
it comes to predictions and therefore can achieve a much greater RMSE score.
```
| null | null | null | null | null | null | null | null | null | null | null | null | Taoshi/model_v4 | [
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silviachen46/llama-test-model | silviachen46 | 2023-11-29T21:15:20Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:15:20Z | 2023-11-29T21:15:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | silviachen46/llama-test-model | [
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Miyuutsu/random_mirrors | Miyuutsu | 2023-11-29T22:53:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:53:59Z | 2023-11-29T21:15:47.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Miyuutsu/random_mirrors | [
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Yilomomo/heussRVC | Yilomomo | 2023-11-29T21:17:53Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T21:17:53Z | 2023-11-29T21:16:20.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Yilomomo/heussRVC | [
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chloe0x0/spliceGPT-medium | chloe0x0 | 2023-11-29T21:16:58Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:16:58Z | 2023-11-29T21:16:58.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chloe0x0/spliceGPT-medium | [
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chloe0x0/spliceGPT_medium | chloe0x0 | 2023-11-29T21:19:07Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:19:07Z | 2023-11-29T21:18:12.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | chloe0x0/spliceGPT_medium | [
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Anonimus-93/1 | Anonimus-93 | 2023-11-29T21:20:11Z | 0 | 0 | null | [
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jordandavis/jaxx_brown_marble_side_table | jordandavis | 2023-11-29T21:21:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:21:22Z | 2023-11-29T21:21:22.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jordandavis/jaxx_brown_marble_side_table | [
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BENBENBENb/finetune_cs_1 | BENBENBENb | 2023-11-29T21:24:34Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | 2023-11-29T21:24:34Z | 2023-11-29T21:21:57.000Z | null | null | ---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: finetune_cs_1
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. -->
# finetune_cs_1
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6272
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6907 | 1.0 | 150 | 1.6272 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | BENBENBENb/finetune_cs_1 | [
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xz-huggingface-0/llama2-7b-dpo-lora-20231129-52 | xz-huggingface-0 | 2023-11-29T21:22:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:22:44Z | 2023-11-29T21:22:44.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | xz-huggingface-0/llama2-7b-dpo-lora-20231129-52 | [
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MikkelWK/DanSumT5-largeV_64175 | MikkelWK | 2023-11-30T01:13:44Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:Danish-summarisation/DanSumT5-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:13:44Z | 2023-11-29T21:23:36.000Z | null | null | ---
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-large
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-largeV_17400
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. -->
# DanSumT5-largeV_17400
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-large](https://huggingface.co/Danish-summarisation/DanSumT5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0810
- Rouge1: 36.0646
- Rouge2: 12.4066
- Rougel: 22.4673
- Rougelsum: 33.2078
- Gen Len: 123.5882
## 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: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 475 | 2.1203 | 34.1656 | 11.1331 | 21.1224 | 31.5873 | 125.4496 |
| 2.4202 | 2.0 | 950 | 2.0419 | 34.8716 | 11.7072 | 21.505 | 32.1799 | 125.6176 |
| 2.1028 | 3.0 | 1425 | 2.0042 | 35.4223 | 12.013 | 21.9284 | 32.6504 | 123.1345 |
| 1.9178 | 4.0 | 1901 | 1.9826 | 35.0313 | 11.7595 | 21.8848 | 32.3896 | 124.3319 |
| 1.7904 | 5.0 | 2376 | 1.9778 | 35.5915 | 12.0513 | 21.9645 | 32.7786 | 124.3697 |
| 1.6758 | 6.0 | 2851 | 1.9829 | 35.0434 | 11.7049 | 21.7988 | 32.2543 | 123.2269 |
| 1.5734 | 7.0 | 3326 | 1.9728 | 35.4344 | 12.0117 | 22.1228 | 32.7301 | 124.0504 |
| 1.5131 | 8.0 | 3802 | 1.9914 | 35.9572 | 12.3134 | 22.1358 | 33.316 | 123.5378 |
| 1.4325 | 9.0 | 4277 | 2.0006 | 35.6071 | 12.0835 | 22.0644 | 32.9752 | 124.2605 |
| 1.3839 | 10.0 | 4752 | 2.0073 | 35.6042 | 12.2947 | 22.4712 | 33.0487 | 122.1975 |
| 1.3156 | 11.0 | 5227 | 2.0149 | 35.8126 | 11.9867 | 22.3753 | 33.0969 | 124.5378 |
| 1.2942 | 12.0 | 5703 | 2.0355 | 35.4593 | 11.9684 | 22.1591 | 32.8905 | 123.2521 |
| 1.2457 | 13.0 | 6178 | 2.0462 | 35.867 | 12.1835 | 22.3972 | 33.2583 | 123.437 |
| 1.2118 | 14.0 | 6653 | 2.0495 | 35.8893 | 12.3608 | 22.307 | 33.1317 | 124.1261 |
| 1.2014 | 15.0 | 7128 | 2.0594 | 35.6055 | 12.1161 | 22.3591 | 32.9453 | 122.2899 |
| 1.181 | 16.0 | 7604 | 2.0745 | 35.8722 | 12.2746 | 22.5422 | 33.1828 | 122.9076 |
| 1.1514 | 17.0 | 8079 | 2.0788 | 35.9651 | 12.3413 | 22.6585 | 33.2246 | 123.2017 |
| 1.1576 | 17.99 | 8550 | 2.0810 | 36.0646 | 12.4066 | 22.4673 | 33.2078 | 123.5882 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | MikkelWK/DanSumT5-largeV_64175 | [
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chenchenchuyi/chen | chenchenchuyi | 2023-11-29T21:24:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:24:31Z | 2023-11-29T21:24:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chenchenchuyi/chen | [
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MikkelWK/DanSumT5-baseV_76992 | MikkelWK | 2023-11-30T01:10:40Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:Danish-summarisation/DanSumT5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:10:40Z | 2023-11-29T21:24:40.000Z | null | null | ---
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: DanSumT5-baseV_76992
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. -->
# DanSumT5-baseV_76992
This model is a fine-tuned version of [Danish-summarisation/DanSumT5-base](https://huggingface.co/Danish-summarisation/DanSumT5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0933
- Rouge1: 35.2623
- Rouge2: 12.3928
- Rougel: 22.0648
- Rougelsum: 32.8779
- Gen Len: 126.0464
## 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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 11
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 158 | 2.2730 | 34.4493 | 10.622 | 20.8333 | 31.7746 | 126.481 |
| No log | 2.0 | 317 | 2.2082 | 35.0683 | 11.7208 | 21.5695 | 32.5233 | 125.962 |
| No log | 3.0 | 475 | 2.1706 | 34.5353 | 11.4258 | 21.3463 | 32.2029 | 125.8017 |
| 2.4623 | 4.0 | 634 | 2.1445 | 34.8325 | 11.8863 | 21.5588 | 32.5282 | 125.0211 |
| 2.4623 | 5.0 | 792 | 2.1246 | 35.3252 | 12.267 | 21.8703 | 33.036 | 125.6624 |
| 2.4623 | 6.0 | 951 | 2.1169 | 34.6852 | 11.8634 | 21.675 | 32.3587 | 126.2532 |
| 2.2087 | 7.0 | 1109 | 2.1056 | 35.0329 | 11.9343 | 22.0452 | 32.7684 | 125.1013 |
| 2.2087 | 8.0 | 1268 | 2.1009 | 35.1984 | 12.2518 | 21.9551 | 32.7163 | 125.7384 |
| 2.2087 | 9.0 | 1426 | 2.0939 | 35.603 | 12.6125 | 22.2904 | 33.2536 | 125.8987 |
| 2.1134 | 10.0 | 1585 | 2.0927 | 35.3434 | 12.277 | 22.0783 | 33.022 | 125.827 |
| 2.1134 | 10.97 | 1738 | 2.0933 | 35.2623 | 12.3928 | 22.0648 | 32.8779 | 126.0464 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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pavlokyrylenko/gbert-base-germaner | pavlokyrylenko | 2023-11-29T21:26:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:26:12Z | 2023-11-29T21:26:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pavlokyrylenko/gbert-base-germaner | [
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TahaCakir/enhanced_turkishReviews-generativeAI | TahaCakir | 2023-11-29T21:27:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:27:11Z | 2023-11-29T21:27:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | TahaCakir/enhanced_turkishReviews-generativeAI | [
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mdgossett/sd_1-5_output_30_images_batch_size_32_V2 | mdgossett | 2023-11-29T21:27:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:27:53Z | 2023-11-29T21:27:53.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mdgossett/sd_1-5_output_30_images_batch_size_32_V2 | [
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Realgon/roberta_sst5_padding70model | Realgon | 2023-11-29T22:49:23Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:49:23Z | 2023-11-29T21:28:03.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding70model
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. -->
# roberta_sst5_padding70model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5077
- Accuracy: 0.5462
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3088 | 1.0 | 534 | 1.2556 | 0.4167 |
| 1.0332 | 2.0 | 1068 | 1.0037 | 0.5593 |
| 0.8699 | 3.0 | 1602 | 1.0976 | 0.5466 |
| 0.7538 | 4.0 | 2136 | 1.1056 | 0.5602 |
| 0.6132 | 5.0 | 2670 | 1.2243 | 0.5561 |
| 0.5149 | 6.0 | 3204 | 1.4017 | 0.5475 |
| 0.4092 | 7.0 | 3738 | 1.6807 | 0.5439 |
| 0.3229 | 8.0 | 4272 | 1.6478 | 0.5525 |
| 0.296 | 9.0 | 4806 | 1.9343 | 0.5516 |
| 0.2412 | 10.0 | 5340 | 1.9477 | 0.5389 |
| 0.231 | 11.0 | 5874 | 2.4445 | 0.5561 |
| 0.2151 | 12.0 | 6408 | 2.6489 | 0.5566 |
| 0.1803 | 13.0 | 6942 | 2.9230 | 0.5507 |
| 0.1543 | 14.0 | 7476 | 3.0641 | 0.5502 |
| 0.1257 | 15.0 | 8010 | 3.1964 | 0.5516 |
| 0.1008 | 16.0 | 8544 | 3.2186 | 0.5548 |
| 0.0865 | 17.0 | 9078 | 3.3259 | 0.5475 |
| 0.065 | 18.0 | 9612 | 3.4088 | 0.5525 |
| 0.067 | 19.0 | 10146 | 3.4669 | 0.5443 |
| 0.0549 | 20.0 | 10680 | 3.5077 | 0.5462 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst5_padding70model | [
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chrisgru/llama2-7b-test | chrisgru | 2023-11-29T22:08:04Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T22:08:04Z | 2023-11-29T21:28:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | chrisgru/llama2-7b-test | [
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joelmgallant/corgy_dog_LoRA | joelmgallant | 2023-11-29T21:29:06Z | 0 | 0 | null | [
"diffusers",
"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"
] | 2023-11-29T21:29:06Z | 2023-11-29T21:29:06.000Z | null | null |
---
tags:
- 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 photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - joelmgallant/corgy_dog_LoRA
<Gallery />
## Model description
These are joelmgallant/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](joelmgallant/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | joelmgallant/corgy_dog_LoRA | [
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xz-huggingface-0/llama2-7b-dpo-lora-20231129-32 | xz-huggingface-0 | 2023-11-29T21:29:54Z | 0 | 0 | null | [
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] | 2023-11-29T21:29:54Z | 2023-11-29T21:29:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | xz-huggingface-0/llama2-7b-dpo-lora-20231129-32 | [
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Jamessjunk/KiryuCrying | Jamessjunk | 2023-11-29T21:31:02Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-29T21:31:02Z | 2023-11-29T21:30:03.000Z | null | null | ---
license: other
license_name: gffdfd
license_link: LICENSE
---
| null | null | null | null | null | null | null | null | null | null | null | null | Jamessjunk/KiryuCrying | [
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BENBENBENb/finetune_cs_20 | BENBENBENb | 2023-11-29T22:20:16Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:lmsys/vicuna-7b-v1.5",
"license:llama2",
"region:us"
] | 2023-11-29T22:20:16Z | 2023-11-29T21:30:45.000Z | null | null | ---
license: llama2
base_model: lmsys/vicuna-7b-v1.5
tags:
- generated_from_trainer
model-index:
- name: finetune_cs_20
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. -->
# finetune_cs_20
This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9803
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7173 | 1.0 | 150 | 1.6256 |
| 1.5959 | 2.0 | 300 | 1.6775 |
| 0.5699 | 3.0 | 450 | 1.9236 |
| 0.4921 | 4.0 | 600 | 2.1318 |
| 0.3044 | 5.0 | 750 | 2.2606 |
| 0.2809 | 6.0 | 900 | 2.3494 |
| 0.2299 | 7.0 | 1050 | 2.4316 |
| 0.2073 | 8.0 | 1200 | 2.4576 |
| 0.1851 | 9.0 | 1350 | 2.4981 |
| 0.1906 | 10.0 | 1500 | 2.6060 |
| 0.1616 | 11.0 | 1650 | 2.6427 |
| 0.1529 | 12.0 | 1800 | 2.6856 |
| 0.1453 | 13.0 | 1950 | 2.7683 |
| 0.1507 | 14.0 | 2100 | 2.7889 |
| 0.1607 | 15.0 | 2250 | 2.8477 |
| 0.1545 | 16.0 | 2400 | 2.8710 |
| 0.1598 | 17.0 | 2550 | 2.8968 |
| 0.1593 | 18.0 | 2700 | 2.9389 |
| 0.1479 | 19.0 | 2850 | 2.9629 |
| 0.1298 | 20.0 | 3000 | 2.9803 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | BENBENBENb/finetune_cs_20 | [
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LoneStriker/deepseek-llm-67b-chat-4.65bpw-h6-exl2 | LoneStriker | 2023-11-30T00:09:50Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:09:50Z | 2023-11-29T21:31:10.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-llm-67b-chat-4.65bpw-h6-exl2 | [
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jordandavis/domes_black_marble_table_lamp | jordandavis | 2023-11-29T21:31:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:31:36Z | 2023-11-29T21:31:36.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jordandavis/domes_black_marble_table_lamp | [
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mari7944/mamont | mari7944 | 2023-11-29T21:33:41Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T21:33:41Z | 2023-11-29T21:32:11.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | mari7944/mamont | [
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bawaa2722/PII_Hornet_NER | bawaa2722 | 2023-11-29T21:35:52Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T21:35:52Z | 2023-11-29T21:35:52.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | bawaa2722/PII_Hornet_NER | [
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ErnestBeckham/gpt-2-medqa | ErnestBeckham | 2023-11-29T21:37:04Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:37:04Z | 2023-11-29T21:36:14.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ErnestBeckham/gpt-2-medqa | [
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MarekW/ShindenSkins | MarekW | 2023-11-29T22:08:45Z | 0 | 0 | null | [
"license:unknown",
"region:us"
] | 2023-11-29T22:08:45Z | 2023-11-29T21:36:59.000Z | null | null | ---
license: unknown
---
| null | null | null | null | null | null | null | null | null | null | null | null | MarekW/ShindenSkins | [
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LoneStriker/deepseek-coder-33b-instruct-3.0bpw-h6-exl2 | LoneStriker | 2023-11-29T21:39:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:39:12Z | 2023-11-29T21:39:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-3.0bpw-h6-exl2 | [
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LoneStriker/deepseek-coder-33b-instruct-4.0bpw-h6-exl2 | LoneStriker | 2023-11-29T21:39:45Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:39:45Z | 2023-11-29T21:39:45.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-4.0bpw-h6-exl2 | [
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abdulmatinomotoso/distilroberta-topic-classification_2 | abdulmatinomotoso | 2023-11-29T22:05:25Z | 0 | 0 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:05:25Z | 2023-11-29T21:41:52.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | abdulmatinomotoso/distilroberta-topic-classification_2 | [
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galatolo/cerbero-7b-openchat | galatolo | 2023-11-29T22:27:25Z | 0 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"it",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:27:25Z | 2023-11-29T21:50:49.000Z | null | null | ---
license: apache-2.0
language:
- it
- en
pipeline_tag: text-generation
--- | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | galatolo/cerbero-7b-openchat | [
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Hazza1/Makarov | Hazza1 | 2023-11-29T21:53:58Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T21:53:58Z | 2023-11-29T21:52:46.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Hazza1/Makarov | [
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michaelfeil/ct2fast-phi-1_5 | michaelfeil | 2023-11-29T22:22:50Z | 0 | 0 | null | [
"transformers",
"phi",
"text-generation",
"ctranslate2",
"int8",
"float16",
"custom_code",
"en",
"arxiv:2309.05463",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:22:50Z | 2023-11-29T21:55:30.000Z | null | null | ---
tags:
- ctranslate2
- int8
- float16
license: other
license_name: microsoft-research-license
license_link: https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx
language:
- en
pipeline_tag: text-generation
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5)
```bash
pip install hf-hub-ctranslate2>=2.12.0 ctranslate2>=3.17.1
```
```python
# from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-phi-1_5"
from hf_hub_ctranslate2 import GeneratorCT2fromHfHub
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
# tokenizer=AutoTokenizer.from_pretrained("{ORG}/{NAME}")
)
outputs = model.generate(
text=["def fibonnaci(", "User: How are you doing? Bot:"],
max_length=64,
include_prompt_in_result=False
)
print(outputs)
```
Checkpoint compatible to [ctranslate2>=3.22.0](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.12.0](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
Converted on 2023-11-29 using
```
TransformersConverter(
"microsoft/phi-1_5",
activation_scales=None,
copy_files=['vocab.json', 'tokenizer.json', 'generation_config.json', 'README.md', 'special_tokens_map.json', 'merges.txt', 'Research License.docx', 'tokenizer_config.json', 'added_tokens.json', '.gitattributes'],
load_as_float16=True,
revision=None,
low_cpu_mem_usage=True,
trust_remote_code=True,
).convert(
output_dir=str(tmp_dir),
vmap = None,
quantization="int8_float16",
force = True,
)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
## Model Summary
The language model phi-1.5 is a Transformer with **1.3 billion** parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, phi-1.5 demonstrates a nearly state-of-the-art performance among models with less than 10 billion parameters.
We **did not** fine-tune phi-1.5 either for **instruction following or through reinforcement learning from human feedback**. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
For a safer model release, we exclude generic web-crawl data sources such as common-crawl from the training. This strategy prevents direct exposure to potentially harmful online content, enhancing the model's safety without RLHF. However, the model is still vulnerable to generating harmful content. We hope the model can help the research community to further study the safety of language models.
phi-1.5 can write poems, draft emails, create stories, summarize texts, write Python code (such as downloading a Hugging Face transformer model), etc.
## Intended Uses
Given the nature of the training data, phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. Note that phi-1.5, being a base model, often produces irrelevant text following the main answer. In the following example, we've truncated the answer for illustrative purposes only.
#### QA format:
```markdown
Write a detailed analogy between mathematics and a lighthouse.
Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home.
```
where the model generates the text after "Answer:".
#### Chat format:
```markdown
Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
Alice: That's a good idea. I'll give it a try.
Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time.
Alice: That makes sense. I'll try that too.
Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective.
Alice: Thanks for the advice, guys. I feel more motivated now.
Charlie: No problem, Alice. We're all in this together.
Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.
```
where the model generates the text after the first "Bob:".
#### Code format:
```python
def print_prime(n):
"""
Print all primes between 1 and n
"""
primes = []
for num in range(2, n+1):
is_prime = True
for i in range(2, int(math.sqrt(num))+1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
print(primes)
```
where the model generates the text after the comments.
**Notes**
* phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
* Direct adoption for production tasks is out of the scope of this research project. As a result, phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
## Limitations of phi-1.5
* Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
* Limited Scope for code: If the model generates Python scripts that utilize uncommon packages or scripts in other languages, we strongly recommend users manually verify all API uses.
* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other language outside of English might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
* Potential Societal Biases: Regardless of the safe data used for its training, the model is not entirely free from societal biases. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
* Toxicity: Despite that the model is trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Dataset size: 30B tokens
* Training tokens: 150B tokens
* Precision: fp16
* GPUs: 32xA100-40G
* Training time: 8 days
### Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [flash-attention](https://github.com/HazyResearch/flash-attention)
### License
The model is licensed under the [Research License](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx).
### Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True)
inputs = tokenizer('''```python
def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
If you need to use the model in a lower precision (e.g., FP16), please wrap the model's forward pass with `torch.autocast()`, as follows:
```python
with torch.autocast(model.device.type, dtype=torch.float16, enabled=True):
outputs = model.generate(**inputs, max_length=200)
```
**Remark.** In the generation function, our model currently does not support beam search (`num_beams` > 1).
Furthermore, in the forward pass of the model, we currently do not support outputting hidden states or attention values, or using custom input embeddings (instead of the model's).
### Citation
You can find the paper at https://arxiv.org/abs/2309.05463
```bib
@article{textbooks2,
title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
journal={arXiv preprint arXiv:2309.05463},
year={2023}
}
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | michaelfeil/ct2fast-phi-1_5 | [
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vrvenkatesh/distilgpt2-finetuned-slangQA | vrvenkatesh | 2023-11-29T22:10:26Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:10:26Z | 2023-11-29T21:58:51.000Z | null | null | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-slangQA
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. -->
# distilgpt2-finetuned-slangQA
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5869 | 1.0 | 1005 | 6.4652 |
| 6.2888 | 2.0 | 2010 | 6.2894 |
| 6.206 | 3.0 | 3015 | 6.2463 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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ishitap/bert-base-uncased-finetuned-swag | ishitap | 2023-11-29T22:19:29Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:19:29Z | 2023-11-29T22:00:54.000Z | null | null | Entry not found | null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | ishitap/bert-base-uncased-finetuned-swag | [
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rthatha/sd-v1-2_monza-finetuned-model | rthatha | 2023-11-29T22:02:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:02:40Z | 2023-11-29T22:02:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rthatha/sd-v1-2_monza-finetuned-model | [
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jordandavis/edie_rectangular_rattan_coffee_table | jordandavis | 2023-11-29T22:02:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:02:54Z | 2023-11-29T22:02:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jordandavis/edie_rectangular_rattan_coffee_table | [
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alforhad/unt_faqs_model | alforhad | 2023-11-30T01:16:54Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:16:54Z | 2023-11-29T22:06:06.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: unt_faqs_model
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. -->
# unt_faqs_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1017
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 12 | 5.4252 |
| No log | 2.0 | 24 | 4.4929 |
| No log | 3.0 | 36 | 3.8593 |
| No log | 4.0 | 48 | 3.1898 |
| No log | 5.0 | 60 | 3.2708 |
| No log | 6.0 | 72 | 3.2766 |
| No log | 7.0 | 84 | 3.6022 |
| No log | 8.0 | 96 | 3.8336 |
| No log | 9.0 | 108 | 4.0215 |
| No log | 10.0 | 120 | 4.1909 |
| No log | 11.0 | 132 | 4.4297 |
| No log | 12.0 | 144 | 4.3085 |
| No log | 13.0 | 156 | 4.4102 |
| No log | 14.0 | 168 | 4.4956 |
| No log | 15.0 | 180 | 4.8586 |
| No log | 16.0 | 192 | 4.8999 |
| No log | 17.0 | 204 | 4.6935 |
| No log | 18.0 | 216 | 4.5933 |
| No log | 19.0 | 228 | 4.5490 |
| No log | 20.0 | 240 | 4.7068 |
| No log | 21.0 | 252 | 4.7150 |
| No log | 22.0 | 264 | 4.9225 |
| No log | 23.0 | 276 | 4.7852 |
| No log | 24.0 | 288 | 4.8968 |
| No log | 25.0 | 300 | 4.9657 |
| No log | 26.0 | 312 | 5.0764 |
| No log | 27.0 | 324 | 5.0940 |
| No log | 28.0 | 336 | 5.1408 |
| No log | 29.0 | 348 | 5.3001 |
| No log | 30.0 | 360 | 5.1304 |
| No log | 31.0 | 372 | 5.4316 |
| No log | 32.0 | 384 | 5.3884 |
| No log | 33.0 | 396 | 5.4434 |
| No log | 34.0 | 408 | 5.3642 |
| No log | 35.0 | 420 | 5.5924 |
| No log | 36.0 | 432 | 5.2098 |
| No log | 37.0 | 444 | 5.3167 |
| No log | 38.0 | 456 | 5.2808 |
| No log | 39.0 | 468 | 5.3687 |
| No log | 40.0 | 480 | 5.4625 |
| No log | 41.0 | 492 | 5.4122 |
| 0.6438 | 42.0 | 504 | 5.4850 |
| 0.6438 | 43.0 | 516 | 5.4797 |
| 0.6438 | 44.0 | 528 | 5.8061 |
| 0.6438 | 45.0 | 540 | 5.8501 |
| 0.6438 | 46.0 | 552 | 5.6079 |
| 0.6438 | 47.0 | 564 | 5.6625 |
| 0.6438 | 48.0 | 576 | 5.7005 |
| 0.6438 | 49.0 | 588 | 5.6703 |
| 0.6438 | 50.0 | 600 | 5.5704 |
| 0.6438 | 51.0 | 612 | 5.7556 |
| 0.6438 | 52.0 | 624 | 5.6541 |
| 0.6438 | 53.0 | 636 | 5.7571 |
| 0.6438 | 54.0 | 648 | 5.8092 |
| 0.6438 | 55.0 | 660 | 5.8529 |
| 0.6438 | 56.0 | 672 | 5.7974 |
| 0.6438 | 57.0 | 684 | 6.0617 |
| 0.6438 | 58.0 | 696 | 5.8630 |
| 0.6438 | 59.0 | 708 | 5.8652 |
| 0.6438 | 60.0 | 720 | 5.9569 |
| 0.6438 | 61.0 | 732 | 6.0163 |
| 0.6438 | 62.0 | 744 | 5.8635 |
| 0.6438 | 63.0 | 756 | 6.1112 |
| 0.6438 | 64.0 | 768 | 5.9750 |
| 0.6438 | 65.0 | 780 | 5.7267 |
| 0.6438 | 66.0 | 792 | 5.9968 |
| 0.6438 | 67.0 | 804 | 5.9974 |
| 0.6438 | 68.0 | 816 | 5.9145 |
| 0.6438 | 69.0 | 828 | 5.8521 |
| 0.6438 | 70.0 | 840 | 5.9012 |
| 0.6438 | 71.0 | 852 | 5.9272 |
| 0.6438 | 72.0 | 864 | 5.9137 |
| 0.6438 | 73.0 | 876 | 5.9371 |
| 0.6438 | 74.0 | 888 | 5.9811 |
| 0.6438 | 75.0 | 900 | 6.0054 |
| 0.6438 | 76.0 | 912 | 6.0101 |
| 0.6438 | 77.0 | 924 | 6.0301 |
| 0.6438 | 78.0 | 936 | 5.9783 |
| 0.6438 | 79.0 | 948 | 5.9784 |
| 0.6438 | 80.0 | 960 | 6.0630 |
| 0.6438 | 81.0 | 972 | 6.1151 |
| 0.6438 | 82.0 | 984 | 6.0869 |
| 0.6438 | 83.0 | 996 | 6.0823 |
| 0.0228 | 84.0 | 1008 | 6.0805 |
| 0.0228 | 85.0 | 1020 | 6.0722 |
| 0.0228 | 86.0 | 1032 | 6.0623 |
| 0.0228 | 87.0 | 1044 | 6.0561 |
| 0.0228 | 88.0 | 1056 | 6.0367 |
| 0.0228 | 89.0 | 1068 | 6.0342 |
| 0.0228 | 90.0 | 1080 | 6.0461 |
| 0.0228 | 91.0 | 1092 | 6.0646 |
| 0.0228 | 92.0 | 1104 | 6.0780 |
| 0.0228 | 93.0 | 1116 | 6.0926 |
| 0.0228 | 94.0 | 1128 | 6.0963 |
| 0.0228 | 95.0 | 1140 | 6.1009 |
| 0.0228 | 96.0 | 1152 | 6.1044 |
| 0.0228 | 97.0 | 1164 | 6.1015 |
| 0.0228 | 98.0 | 1176 | 6.1014 |
| 0.0228 | 99.0 | 1188 | 6.1021 |
| 0.0228 | 100.0 | 1200 | 6.1017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | alforhad/unt_faqs_model | [
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TheBloke/Iambe-20B-DARE-GPTQ | TheBloke | 2023-11-29T23:13:28Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:athirdpath/Iambe-20b-DARE",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T23:13:28Z | 2023-11-29T22:06:26.000Z | null | null | ---
base_model: athirdpath/Iambe-20b-DARE
inference: false
license: cc-by-nc-4.0
model_creator: LadyBabyloin
model_name: Iambe 20B Dare
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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# Iambe 20B Dare - GPTQ
- Model creator: [LadyBabyloin](https://huggingface.co/athirdpath)
- Original model: [Iambe 20B Dare](https://huggingface.co/athirdpath/Iambe-20b-DARE)
<!-- description start -->
# Description
This repo contains GPTQ model files for [LadyBabyloin's Iambe 20B Dare](https://huggingface.co/athirdpath/Iambe-20b-DARE).
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/Iambe-20B-DARE-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF)
* [LadyBabyloin's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/athirdpath/Iambe-20b-DARE)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [LadyBabyloin's Iambe 20B Dare](https://huggingface.co/athirdpath/Iambe-20b-DARE).
<!-- licensing 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/Iambe-20B-DARE-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.52 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.89 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/Iambe-20B-DARE-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 12.04 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.41 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.35 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 9.51 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Iambe-20B-DARE-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.80 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher 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/Iambe-20B-DARE-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Iambe-20B-DARE-GPTQ:gptq-4bit-128g-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 `Iambe-20B-DARE-GPTQ`:
```shell
mkdir Iambe-20B-DARE-GPTQ
huggingface-cli download TheBloke/Iambe-20B-DARE-GPTQ --local-dir Iambe-20B-DARE-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Iambe-20B-DARE-GPTQ
huggingface-cli download TheBloke/Iambe-20B-DARE-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Iambe-20B-DARE-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 Iambe-20B-DARE-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Iambe-20B-DARE-GPTQ --local-dir Iambe-20B-DARE-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-128g-actorder_True https://huggingface.co/TheBloke/Iambe-20B-DARE-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/Iambe-20B-DARE-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Iambe-20B-DARE-GPTQ:gptq-4bit-128g-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: `Iambe-20B-DARE-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/Iambe-20B-DARE-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### 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/Iambe-20B-DARE-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### 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: LadyBabyloin's Iambe 20B Dare
<p align="center"><img src="https://i.ibb.co/pbpJHpk/iambe-sml.png"/><font size="6"> <b>Iambe-20b-DARE</b> </font></p>
## Description and Role
Named after a charming daughter of Echo and Pan in Greek myth, Iambe-20b-DARE is a [DARE](https://github.com/yule-BUAA/MergeLM) merge building on my recent experiments.
Iambe is intended to have the best realistically possible understanding of anatomy and of a scene's state for a 20b merge, while remaining personable and authentic in "voice".
## Prompting and Context
Iambe-20b-DARE uses Alpaca formatting, and has an effective context size of 4096 tokens. This model is uncensored, and the output/deployment of this model is the responsibility of the user.
## Method and Hypothesis
Based on my extended vanilla model [BigLlama](https://huggingface.co/athirdpath/BigLlama-20b), this adds elements of:
- [NeverSleep/Noromaid-20b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1) - Addded to adapt the excellent writing and "soul" that come from the datasets backing this model.
- [athirdpath/Eileithyia-20b](https://huggingface.co/athirdpath/Eileithyia-20b) - Added at low weight and density to capture anatomical data and its relation to fiction without the model's other... quirks.
- [athirdpath/CleverGirl-20b-Blended](https://huggingface.co/athirdpath/CleverGirl-20b-Blended) - Added to capture CleverGirl's problem-solving abilities.
<p align="center"><font size="7"> <b>Examples</b></font>
<p align="center"><font size="3"> <i>(q5_k_m GGUF quant, Ooba textgen, Midnight Enigma preset for KISS reasons)</i></font>
<p align="center"><font size="5"> Reasoning and Recall - STEM</font><img src="https://i.ibb.co/YDkRD43/base.png"/>
<p align="center"><font size="5"> Reasoning and Recall - Social Science</font><img src="https://i.ibb.co/L0kc3pb/Screenshot-2023-11-29-024037.png"/>
<p align="center"><font size="5"> Ethics and Prompt Interpretation</font><img src="https://i.ibb.co/b5vH6jD/Screenshot-2023-11-29-040408.png"/>
<p align="center"><font size="5">Role-Playing, Complex Card</font>
<p align="center"><font size="3">SillyTavern, Roleplay instruct preset, just Min_P 0.1 and Temp 1.2</font><img src="https://i.ibb.co/NrKNn2j/Screenshot-2023-11-29-051923.png"/>
<p align="center"><font size="6"><b><a href="https://i.ibb.co/m5G0ZVp/Screenshot-2023-11-29-004705.png">!!NSFW!! - Erotica Writing Example - !!NSFW!!</font></a></b></p>
## Testing and Conclusions
VERY Impressed so far, concrete data coming eventually.
Does still have some confusion (at q5_k_m), but has instantly become my daily driver.
## Recipe
merge_method: dare_ties
- base_model: athirdpath/BigLlama-20b
- model: NeverSleep/Noromaid-20b-v0.1.1
weight: 0.41 / density: 0.50
- model: athirdpath/Eileithyia-20b
weight: 0.18 / density: 0.30
- model: athirdpath/CleverGirl-20b-Blended
weight: 0.41 / density: 0.50
int8_mask: true
dtype: bfloat16
## Gratitude
Thanks to brucethemoose for the recipe. Thanks to Undi95 and IkariDev at NeverSleep for Noromaid, as well as lots of inspiration. Thanks to Sao10K for [half of](https://huggingface.co/Sao10K/Mythical-Destroyer-V2-L2-13B) CleverGirl.
## Geneology Credits - Models and LoRAs, 3 levels deep
- athirdpath/BigLlama-20b
- TheBloke/Llama-2-13B-fp16
- iamshnoo/alpaca-2-13b-english
- NeverSleep/Noromaid-20b-v0.1.1
- Aesir Private RP dataset
- HuggingFaceH4/no_robots
- athirdpath/CleverGirl-20b-Blended
- athirdpath/Orca-2-13b-Alpaca-Uncensored
- microsoft/Orca-2-13b
- athirdpath/Orca-2-13b-Alpaca-Uncensored-LORA
- Sao10K/Mythical-Destroyer-V2-L2-13B
- TheBloke/Llama-2-13B-fp16
- Gryphe/MythoMax-L2-13b
- totally-not-an-llm/PuddleJumper-13b
- jondurbin/airoboros-l2-13b-2.1
- rombodawg/LosslessMegaCoder-llama2-13b-mini
- The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
- athirdpath/Eileithyia-20b
- athirdpath/Harmonia-20B
- Undi95/Emerhyst-20B
- Undi95/Emerald-13B
- Undi95/Amethyst-13B
- Undi95/MXLewd-L2-20B
- Undi95/MLewd-L2-13B-v2-3
- Undi95/ReMM-v2.1-L2-13B
- Xwin-LM/Xwin-LM-13B-V0.1
- Undi95/Lewd-Sydney-20B
- Free_Sydney_V2_13b_HF
- Undi95/Xwin-MLewd-13B-V0.2
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- athirdpath/Nethena-20b-Glued
- NeverSleep/Nethena-20B
- athirdpath/Nethena-20b-Glue-LORA
- tavtav/Rose-20B
- CalderaAI/13B-Thorns-l2
- NeverSleep/Noromaid-13b-v0.1.0
- Undi95/PsyMedRP-v1-20B
- jondurbin/airoboros-l2-13b-3.0
- ehartford/Samantha-1.11-13b
- Xwin-LM/Xwin-LM-13B-V0.1
- chaoyi-wu/MedLLaMA_13B
- migtissera/Synthia-13B-v1.2
- NeverSleep/Noromaid-20b-v0.1.1
- Aesir Private RP dataset
- HuggingFaceH4/no_robots
- Undi95/U-Amethyst-20B
- Xwin-LM/Xwin-LM-13B-V0.1
- The-Face-Of-Goonery/Huginn-13b-FP16
- zattio770/120-Days-of-LORA-v2-13B
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Undi95/Unholy-v1-12L-13B
- athirdpath/Eileithyia-20b-LORA
Thanks again to everyone involved.
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/Iambe-20B-DARE-GPTQ | [
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codegood/results | codegood | 2023-11-30T01:26:41Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:amazon/MistralLite",
"license:apache-2.0",
"region:us"
] | 2023-11-30T01:26:41Z | 2023-11-29T22:07:56.000Z | null | null | ---
license: apache-2.0
base_model: amazon/MistralLite
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [amazon/MistralLite](https://huggingface.co/amazon/MistralLite) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0155
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 600
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2156 | 0.04 | 100 | 2.4828 |
| 2.5508 | 0.07 | 200 | 2.1809 |
| 2.4316 | 0.11 | 300 | 2.1147 |
| 2.3422 | 0.15 | 400 | 2.0451 |
| 2.3694 | 0.18 | 500 | 2.0248 |
| 2.3402 | 0.22 | 600 | 2.0155 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | codegood/results | [
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gasmartin/qa-squad-roberta-based | gasmartin | 2023-11-29T22:09:02Z | 0 | 0 | null | [
"license:mit",
"region:us",
"has_space"
] | 2023-11-29T22:09:02Z | 2023-11-29T22:09:02.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | gasmartin/qa-squad-roberta-based | [
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vrvenkatesh/opt-350m-finetuned-slangQA | vrvenkatesh | 2023-11-29T23:13:01Z | 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"
] | 2023-11-29T23:13:01Z | 2023-11-29T22:14:40.000Z | null | null | ---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: opt-350m-finetuned-slangQA
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. -->
# opt-350m-finetuned-slangQA
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2105 | 1.0 | 1020 | 3.1122 |
| 2.7784 | 2.0 | 2040 | 3.0349 |
| 2.5066 | 3.0 | 3060 | 3.0236 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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TheOriginalMarcelo/my_awesome_model | TheOriginalMarcelo | 2023-11-29T22:53:37Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:53:37Z | 2023-11-29T22:14:44.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.92688
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1955
- Accuracy: 0.9269
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2164 | 1.0 | 1563 | 0.1955 | 0.9269 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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abdulmatinomotoso/distilroberta-topic-classification_3 | abdulmatinomotoso | 2023-11-29T22:37:59Z | 0 | 0 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:37:59Z | 2023-11-29T22:14:51.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | abdulmatinomotoso/distilroberta-topic-classification_3 | [
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neopolita/ppo-LunarLander-v2 | neopolita | 2023-11-29T22:16:56Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T22:16:56Z | 2023-11-29T22:16:40.000Z | 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: 255.42 +/- 23.29
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 | neopolita/ppo-LunarLander-v2 | [
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imspidey/LoAQUAinked | imspidey | 2023-11-29T22:18:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:18:11Z | 2023-11-29T22:18:10.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | imspidey/LoAQUAinked | [
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ramij01/NicoSattler | ramij01 | 2023-11-29T22:23:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:23:51Z | 2023-11-29T22:19:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ramij01/NicoSattler | [
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LoneStriker/deepseek-llm-67b-chat-5.0bpw-h6-exl2 | LoneStriker | 2023-11-30T00:10:25Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:10:25Z | 2023-11-29T22:19:12.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-llm-67b-chat-5.0bpw-h6-exl2 | [
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michaelfeil/ct2fast-phi-1 | michaelfeil | 2023-11-29T22:22:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:22:54Z | 2023-11-29T22:22:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | michaelfeil/ct2fast-phi-1 | [
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joyfine/vicuna-7b-fine-tuning_twitter_20_64 | joyfine | 2023-11-29T22:29:07Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:29:07Z | 2023-11-29T22:23:06.000Z | 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_64 | [
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Wayne235f/DialoGPT-small-joshua | Wayne235f | 2023-11-29T23:40:24Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:40:24Z | 2023-11-29T22:24:16.000Z | null | null | ---
tags:
- conversational
---
#DialoGPT model | null | transformers | conversational | null | null | null | null | null | null | null | null | null | Wayne235f/DialoGPT-small-joshua | [
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galatolo/cerbero-7b-openchat-gguf | galatolo | 2023-11-29T22:29:01Z | 0 | 0 | null | [
"text-generation",
"it",
"en",
"license:apache-2.0",
"region:us"
] | 2023-11-29T22:29:01Z | 2023-11-29T22:28:39.000Z | null | null | ---
license: apache-2.0
language:
- it
- en
pipeline_tag: text-generation
--- | null | null | text-generation | null | null | null | null | null | null | null | null | null | galatolo/cerbero-7b-openchat-gguf | [
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stoddur/alexander1 | stoddur | 2023-11-29T22:28:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:28:49Z | 2023-11-29T22:28:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | stoddur/alexander1 | [
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HazimiYusri/test | HazimiYusri | 2023-11-29T22:28:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:28:57Z | 2023-11-29T22:28:57.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | HazimiYusri/test | [
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dhsalazar11/ppo-LunarLander-v2 | dhsalazar11 | 2023-11-29T22:30:18Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T22:30:18Z | 2023-11-29T22:29:49.000Z | 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: 247.58 +/- 55.54
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 | dhsalazar11/ppo-LunarLander-v2 | [
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Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning-2 | Nachuwu | 2023-11-30T01:11:08Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"base_model:Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning",
"license:cc-by-nc-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:11:08Z | 2023-11-29T22:30:12.000Z | null | null | ---
license: cc-by-nc-4.0
base_model: Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-fleur-mms-batch6-epoch16-finetunning-2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: test
args: default
metrics:
- name: Wer
type: wer
value: 0.5148173673328739
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-fleur-mms-batch6-epoch16-finetunning-2
This model is a fine-tuned version of [Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning](https://huggingface.co/Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7540
- Wer: 0.5148
## 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: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 14
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7213 | 0.42 | 100 | 0.8689 | 0.5824 |
| 0.8363 | 0.84 | 200 | 0.9158 | 0.6085 |
| 0.8347 | 1.27 | 300 | 0.9091 | 0.5362 |
| 0.874 | 1.69 | 400 | 0.8487 | 0.5307 |
| 0.7452 | 2.11 | 500 | 0.8507 | 0.5472 |
| 0.791 | 2.53 | 600 | 0.8075 | 0.5410 |
| 0.7681 | 2.95 | 700 | 0.8389 | 0.5575 |
| 0.7645 | 3.38 | 800 | 0.8122 | 0.5610 |
| 0.739 | 3.8 | 900 | 0.8625 | 0.5569 |
| 0.7989 | 4.22 | 1000 | 0.8166 | 0.5624 |
| 0.7618 | 4.64 | 1100 | 0.8646 | 0.5796 |
| 0.7689 | 5.06 | 1200 | 0.8462 | 0.5445 |
| 0.738 | 5.49 | 1300 | 0.8014 | 0.5431 |
| 0.6966 | 5.91 | 1400 | 0.7993 | 0.5217 |
| 0.7275 | 6.33 | 1500 | 0.8049 | 0.5252 |
| 0.7304 | 6.75 | 1600 | 0.7870 | 0.5231 |
| 0.7225 | 7.17 | 1700 | 0.8037 | 0.5472 |
| 0.7148 | 7.59 | 1800 | 0.7913 | 0.5389 |
| 0.7225 | 8.02 | 1900 | 0.7908 | 0.5183 |
| 0.7123 | 8.44 | 2000 | 0.7894 | 0.5245 |
| 0.692 | 8.86 | 2100 | 0.7693 | 0.5224 |
| 0.7855 | 9.28 | 2200 | 0.7614 | 0.5252 |
| 0.6421 | 9.7 | 2300 | 0.7870 | 0.5169 |
| 0.6831 | 10.13 | 2400 | 0.7820 | 0.5121 |
| 0.7293 | 10.55 | 2500 | 0.7640 | 0.5245 |
| 0.6625 | 10.97 | 2600 | 0.7912 | 0.5575 |
| 0.6612 | 11.39 | 2700 | 0.7578 | 0.5203 |
| 0.6873 | 11.81 | 2800 | 0.7579 | 0.5231 |
| 0.7113 | 12.24 | 2900 | 0.7595 | 0.5258 |
| 0.7393 | 12.66 | 3000 | 0.7532 | 0.5148 |
| 0.7297 | 13.08 | 3100 | 0.7532 | 0.5183 |
| 0.6303 | 13.5 | 3200 | 0.7514 | 0.5162 |
| 0.6444 | 13.92 | 3300 | 0.7540 | 0.5148 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Nachuwu/wav2vec2-fleur-mms-batch6-epoch16-finetunning-2 | [
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1.0576143264770508,
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0.2662601172924042,
0.9084045886993408,
0.062476810067892075,
0.09545427560806274,
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0.41469353437423706,
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0.34445735812187195,
0.11591948568820953,
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] |
TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF | TheBloke | 2023-11-29T22:34:20Z | 0 | 3 | null | [
"transformers",
"gguf",
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:34:20Z | 2023-11-29T22:30:14.000Z | null | null | ---
base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
datasets:
- mlabonne/chatml_dpo_pairs
inference: false
language:
- en
license: apache-2.0
model_creator: Maxime Labonne
model_name: NeuralHermes 2.5 Mistral 7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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<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 -->
# NeuralHermes 2.5 Mistral 7B - GGUF
- Model creator: [Maxime Labonne](https://huggingface.co/mlabonne)
- Original model: [NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Maxime Labonne's NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF)
* [Maxime Labonne's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [neuralhermes-2.5-mistral-7b.Q2_K.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [neuralhermes-2.5-mistral-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [neuralhermes-2.5-mistral-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [neuralhermes-2.5-mistral-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [neuralhermes-2.5-mistral-7b.Q4_0.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [neuralhermes-2.5-mistral-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [neuralhermes-2.5-mistral-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [neuralhermes-2.5-mistral-7b.Q5_0.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [neuralhermes-2.5-mistral-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [neuralhermes-2.5-mistral-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [neuralhermes-2.5-mistral-7b.Q6_K.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [neuralhermes-2.5-mistral-7b.Q8_0.gguf](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF/blob/main/neuralhermes-2.5-mistral-7b.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## 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
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF and below it, a specific filename to download, such as: neuralhermes-2.5-mistral-7b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF neuralhermes-2.5-mistral-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF --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 TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF neuralhermes-2.5-mistral-7b.Q4_K_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 -->
## Example `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 neuralhermes-2.5-mistral-7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
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)
## How to run 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%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run 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="./neuralhermes-2.5-mistral-7b.Q4_K_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(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # 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="./neuralhermes-2.5-mistral-7b.Q4_K_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."
}
]
)
```
## How to use 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)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: Maxime Labonne's NeuralHermes 2.5 Mistral 7B
<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
<!-- original-model-card end -->
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TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ | TheBloke | 2023-11-29T22:47:52Z | 0 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T22:47:52Z | 2023-11-29T22:30:14.000Z | null | null | ---
base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
datasets:
- mlabonne/chatml_dpo_pairs
inference: false
language:
- en
license: apache-2.0
model_creator: Maxime Labonne
model_name: NeuralHermes 2.5 Mistral 7B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
---
<!-- 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 -->
# NeuralHermes 2.5 Mistral 7B - AWQ
- Model creator: [Maxime Labonne](https://huggingface.co/mlabonne)
- Original model: [NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
<!-- description start -->
## Description
This repo contains AWQ model files for [Maxime Labonne's NeuralHermes 2.5 Mistral 7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF)
* [Maxime Labonne's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/NeuralHermes-2.5-Mistral-7B-AWQ`.
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: `NeuralHermes-2.5-Mistral-7B-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/NeuralHermes-2.5-Mistral-7B-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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: Maxime Labonne's NeuralHermes 2.5 Mistral 7B
<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
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ElmoVT/q-FrozenLake-v1-4x4-noSlippery | ElmoVT | 2023-11-29T22:30:56Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T22:30:56Z | 2023-11-29T22:30:52.000Z | null | null | ---
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
---
# **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="ElmoVT/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"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | ElmoVT/q-FrozenLake-v1-4x4-noSlippery | [
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sade-adrien/mappingadapter_roberta_mistral | sade-adrien | 2023-11-29T22:39:17Z | 0 | 0 | null | [
"dataset:sade-adrien/redpajama_v2_sample_10M",
"region:us"
] | 2023-11-29T22:39:17Z | 2023-11-29T22:31:52.000Z | null | null | ---
datasets:
- sade-adrien/redpajama_v2_sample_10M
---
MappingAdapter exact structure available in representation_mapping.py
Mapping "sentence-transformers/stsb-roberta-large"'s hidden representation to "mistralai/Mistral-7B-Instruct-v0.1"'s.
Training:
* Steps: 114k
* Gradient accumulation: 2
* Batch size: 64
* Warm-up steps: 100
* Learning Rate: 3e-5 with linear scheduling
* Eval steps: %8000
* Training hours: ~98h
* Eval hours: ~10h
* Gradient updates: 57k
* Train examples: 7.3M
* Eval examples: 106k
* Adapter: Decoder_dim (4096) → 4096 → LeakyRelu(.1) → Encoder_dim (1024) | null | null | null | null | null | null | null | null | null | null | null | null | sade-adrien/mappingadapter_roberta_mistral | [
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ElmoVT/Taxi-v3 | ElmoVT | 2023-11-29T22:36:39Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T22:36:39Z | 2023-11-29T22:33:52.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ElmoVT/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | ElmoVT/Taxi-v3 | [
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LoneStriker/deepseek-coder-33b-instruct-4.65bpw-h6-exl2 | LoneStriker | 2023-11-29T22:34:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:34:14Z | 2023-11-29T22:34:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-4.65bpw-h6-exl2 | [
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LoneStriker/deepseek-coder-33b-instruct-5.0bpw-h6-exl2 | LoneStriker | 2023-11-29T22:34:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:34:17Z | 2023-11-29T22:34:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-5.0bpw-h6-exl2 | [
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kchoi/ziplora-sdxl-girl-disney | kchoi | 2023-11-29T22:35:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:35:43Z | 2023-11-29T22:35:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kchoi/ziplora-sdxl-girl-disney | [
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hkivancoral/smids_1x_beit_base_adamax_001_fold2 | hkivancoral | 2023-11-30T01:28:21Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:28:21Z | 2023-11-29T22:36:03.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_beit_base_adamax_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7903494176372712
---
<!-- 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. -->
# smids_1x_beit_base_adamax_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4865
- Accuracy: 0.7903
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9007 | 1.0 | 75 | 0.8802 | 0.5191 |
| 0.7789 | 2.0 | 150 | 0.8973 | 0.5424 |
| 0.8219 | 3.0 | 225 | 0.7607 | 0.6406 |
| 0.7838 | 4.0 | 300 | 0.7358 | 0.6522 |
| 0.6602 | 5.0 | 375 | 0.6978 | 0.6672 |
| 0.7026 | 6.0 | 450 | 0.6685 | 0.6955 |
| 0.6394 | 7.0 | 525 | 0.7731 | 0.6589 |
| 0.6471 | 8.0 | 600 | 0.6234 | 0.7138 |
| 0.5881 | 9.0 | 675 | 0.6358 | 0.7205 |
| 0.5254 | 10.0 | 750 | 0.5746 | 0.7671 |
| 0.5153 | 11.0 | 825 | 0.5501 | 0.7704 |
| 0.5459 | 12.0 | 900 | 0.5543 | 0.7687 |
| 0.5526 | 13.0 | 975 | 0.5321 | 0.7737 |
| 0.5236 | 14.0 | 1050 | 0.5404 | 0.7937 |
| 0.4317 | 15.0 | 1125 | 0.6220 | 0.7604 |
| 0.4195 | 16.0 | 1200 | 0.5679 | 0.7854 |
| 0.3753 | 17.0 | 1275 | 0.6021 | 0.7687 |
| 0.3821 | 18.0 | 1350 | 0.5958 | 0.7854 |
| 0.3599 | 19.0 | 1425 | 0.6478 | 0.7837 |
| 0.2813 | 20.0 | 1500 | 0.6634 | 0.7671 |
| 0.224 | 21.0 | 1575 | 0.6766 | 0.7820 |
| 0.2635 | 22.0 | 1650 | 0.6781 | 0.7870 |
| 0.1832 | 23.0 | 1725 | 0.8041 | 0.7604 |
| 0.1751 | 24.0 | 1800 | 0.8069 | 0.7671 |
| 0.2421 | 25.0 | 1875 | 0.8820 | 0.7737 |
| 0.2115 | 26.0 | 1950 | 0.8838 | 0.7970 |
| 0.1798 | 27.0 | 2025 | 0.8954 | 0.7787 |
| 0.1341 | 28.0 | 2100 | 1.0505 | 0.7987 |
| 0.0669 | 29.0 | 2175 | 1.2992 | 0.7770 |
| 0.0892 | 30.0 | 2250 | 1.1168 | 0.7987 |
| 0.1159 | 31.0 | 2325 | 1.2066 | 0.7870 |
| 0.1289 | 32.0 | 2400 | 1.5859 | 0.7687 |
| 0.0687 | 33.0 | 2475 | 1.1777 | 0.7887 |
| 0.0226 | 34.0 | 2550 | 1.4423 | 0.7854 |
| 0.04 | 35.0 | 2625 | 1.4594 | 0.7870 |
| 0.0552 | 36.0 | 2700 | 1.3867 | 0.7820 |
| 0.0439 | 37.0 | 2775 | 1.4599 | 0.7720 |
| 0.0308 | 38.0 | 2850 | 1.4968 | 0.7903 |
| 0.0564 | 39.0 | 2925 | 1.5256 | 0.7953 |
| 0.0227 | 40.0 | 3000 | 1.4454 | 0.7953 |
| 0.0214 | 41.0 | 3075 | 1.3100 | 0.8087 |
| 0.0167 | 42.0 | 3150 | 1.4699 | 0.7987 |
| 0.0299 | 43.0 | 3225 | 1.4525 | 0.7903 |
| 0.0171 | 44.0 | 3300 | 1.3889 | 0.8053 |
| 0.011 | 45.0 | 3375 | 1.3819 | 0.7920 |
| 0.014 | 46.0 | 3450 | 1.5122 | 0.7903 |
| 0.0198 | 47.0 | 3525 | 1.4328 | 0.7920 |
| 0.0085 | 48.0 | 3600 | 1.5057 | 0.7920 |
| 0.0028 | 49.0 | 3675 | 1.4856 | 0.7903 |
| 0.0049 | 50.0 | 3750 | 1.4865 | 0.7903 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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jbeomlee93/output_lr1e-6 | jbeomlee93 | 2023-11-29T22:36:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:36:09Z | 2023-11-29T22:36:09.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jbeomlee93/output_lr1e-6 | [
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Preuss01/goulartevoice | Preuss01 | 2023-11-29T22:58:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:58:30Z | 2023-11-29T22:36:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Preuss01/goulartevoice | [
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adenovirux/facebook-opt-6.7b-qlora | adenovirux | 2023-11-29T22:37:40Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:facebook/opt-6.7b",
"region:us"
] | 2023-11-29T22:37:40Z | 2023-11-29T22:37:39.000Z | null | null | ---
library_name: peft
base_model: facebook/opt-6.7b
---
# 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
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#### Preprocessing [optional]
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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:
- 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 | adenovirux/facebook-opt-6.7b-qlora | [
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wolferobert3/mistral_factcheck_peft | wolferobert3 | 2023-11-29T22:37:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:37:41Z | 2023-11-29T22:37:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | wolferobert3/mistral_factcheck_peft | [
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Angelinis/roberta-base-finetuned-enem | Angelinis | 2023-11-29T23:03:54Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:03:54Z | 2023-11-29T22:38:24.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-finetuned-enem
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. -->
# roberta-base-finetuned-enem
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.1924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6341 | 1.0 | 933 | 1.6094 | 0.2090 |
| 1.6251 | 2.0 | 1866 | 1.6094 | 0.2101 |
| 1.6289 | 3.0 | 2799 | 1.6094 | 0.1924 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0
| null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | Angelinis/roberta-base-finetuned-enem | [
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Mariaaaaa/vicuna-7b-fine-tuning_Twitter_20_256 | Mariaaaaa | 2023-11-29T22:45:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:45:45Z | 2023-11-29T22:39:40.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mariaaaaa/vicuna-7b-fine-tuning_Twitter_20_256 | [
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Anasmob/poca-SoccerTwos | Anasmob | 2023-11-29T22:44:19Z | 0 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2023-11-29T22:44:19Z | 2023-11-29T22:43:40.000Z | null | null | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
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: Anasmob/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | Anasmob/poca-SoccerTwos | [
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andrecastro/swin-tiny-patch4-window7-224-finetuned-eurosat | andrecastro | 2023-11-30T01:00:53Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:00:53Z | 2023-11-29T22:44:12.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7149341806627326
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1312
- Accuracy: 0.7149
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1091 | 1.0 | 160 | 1.1345 | 0.6341 |
| 0.0426 | 2.0 | 321 | 1.3777 | 0.6627 |
| 0.0151 | 2.99 | 480 | 1.1312 | 0.7149 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | andrecastro/swin-tiny-patch4-window7-224-finetuned-eurosat | [
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