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csukuangfj/vits-piper-vi_VN-vais1000-medium | csukuangfj | 2023-11-29T13:58:43Z | 0 | 0 | null | [
"onnx",
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] | 2023-11-29T13:58:43Z | 2023-11-29T12:43:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-vi_VN-vais1000-medium | [
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csukuangfj/vits-piper-vi_VN-vivos-x_low | csukuangfj | 2023-11-29T13:58:41Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T13:58:41Z | 2023-11-29T12:43:47.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-vi_VN-vivos-x_low | [
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Seooooooogi/lora-sdxl-orange | Seooooooogi | 2023-11-29T13:39:29Z | 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"
] | 2023-11-29T13:39:29Z | 2023-11-29T12:46:06.000Z | null | null |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'a backpack in szn style'
output:
url:
"image_0.png"
- text: 'a backpack in szn style'
output:
url:
"image_1.png"
- text: 'a backpack in szn style'
output:
url:
"image_2.png"
- text: 'a backpack in szn style'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: szn style
license: openrail++
---
# SDXL LoRA DreamBooth - Seooooooogi/lora-sdxl-orange
<Gallery />
## Model description
These are Seooooooogi/lora-sdxl-orange LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use szn style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Seooooooogi/lora-sdxl-orange/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | Seooooooogi/lora-sdxl-orange | [
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coke7976/test_save | coke7976 | 2023-11-29T12:46:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:46:37Z | 2023-11-29T12:46:37.000Z | null | null | Invalid username or password. | null | null | null | null | null | null | null | null | null | null | null | null | coke7976/test_save | [
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError0percentSeed100 | behzadnet | 2023-11-29T12:48:21Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T12:48:21Z | 2023-11-29T12:48:13.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
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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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## Technical Specifications [optional]
### Model Architecture and Objective
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#### Hardware
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#### Software
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError0percentSeed100 | behzadnet | 2023-11-29T12:48:31Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T12:48:31Z | 2023-11-29T12:48:28.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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Govind35783/Govind | Govind35783 | 2023-11-29T12:54:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:54:01Z | 2023-11-29T12:54:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Govind35783/Govind | [
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DIAS123/DIAS | DIAS123 | 2023-11-29T12:55:15Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T12:55:15Z | 2023-11-29T12:55:14.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | DIAS123/DIAS | [
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anetakahle/OpenChat3.5-km-finetuned | anetakahle | 2023-11-29T13:04:10Z | 0 | 0 | null | [
"text-generation",
"dataset:anetakahle/sexchat_cs1",
"license:mit",
"region:us"
] | 2023-11-29T13:04:10Z | 2023-11-29T12:58:28.000Z | null | null | ---
license: mit
datasets:
- anetakahle/sexchat_cs1
pipeline_tag: text-generation
--- | null | null | text-generation | null | null | null | null | null | null | null | null | null | anetakahle/OpenChat3.5-km-finetuned | [
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caretech-owl/leo-hessianai-7B-ggpq-german-quotes-lora | caretech-owl | 2023-11-29T16:10:32Z | 0 | 0 | null | [
"arxiv:1910.09700",
"base_model:TheBloke/leo-hessianai-7B-GPTQ",
"license:cc-by-4.0",
"region:us"
] | 2023-11-29T16:10:32Z | 2023-11-29T12:59:25.000Z | null | null | ---
base_model: TheBloke/leo-hessianai-7B-GPTQ
license: cc-by-4.0
---
# Model Card for Model ID
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## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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#### Metrics
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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: gptq
- bits: 8
- tokenizer: None
- dataset: None
- group_size: 32
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- use_exllama: True
- max_input_length: None
- exllama_config: {'version': <ExllamaVersion.ONE: 1>}
- cache_block_outputs: True
### Framework versions
- PEFT 0.6.2
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csuhan/OneLLM-7B | csuhan | 2023-11-29T12:59:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:59:42Z | 2023-11-29T12:59:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csuhan/OneLLM-7B | [
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csukuangfj/vits-piper-fi_FI-harri-medium | csukuangfj | 2023-11-29T13:57:02Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T13:57:02Z | 2023-11-29T13:04:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-fi_FI-harri-medium | [
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-1.026215672492981
] |
Luckyluuuc/q-FrozenLake-v1-4x4-noSlippery | Luckyluuuc | 2023-11-29T13:09:40Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T13:09:40Z | 2023-11-29T13:09:36.000Z | 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="Luckyluuuc/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 | Luckyluuuc/q-FrozenLake-v1-4x4-noSlippery | [
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sokada/codegen25-7b-multi-gguf-with-dummy-tokenizer | sokada | 2023-11-29T14:17:13Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"code",
"dataset:bigcode/starcoderdata",
"arxiv:2305.02309",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:17:13Z | 2023-11-29T13:12:01.000Z | null | null | ---
license: apache-2.0
datasets:
- bigcode/starcoderdata
language:
- code
pipeline_tag: text-generation
---
# This repo is a fork for flatline_lsp
See [flatline_lsp](https://github.com/okdshin/flatline_lsp).
This repository is a fork of [Salesforce/codegen25-7b-multi](https://huggingface.co/Salesforce/codegen25-7b-multi).
This repository contains gguf files but its tokenizer is dummy.
---
# CodeGen2.5-7B-multi
Title: [**CodeGen2.5: Small, but mighty**](https://blog.salesforceairesearch.com/codegen25)
Authors: [Erik Nijkamp](https://eriknijkamp.com)\*, [Hiroaki Hayashi](https://hiroakih.me)\*, Yingbo Zhou, Caiming Xiong
(\* equal contribution)
## Model description
[CodeGen2.5](https://github.com/salesforce/CodeGen) is a family of autoregressive language models for **program synthesis**.
Building upon [CodeGen2](https://arxiv.org/abs/2305.02309), the model is trained on [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) for 1.4T tokens, achieving competitive results compared to StarCoderBase-15.5B with less than half the size.
Like CodeGen2, this model is capable of infilling, and supports multiple programming languages.
We then further train on Python, then on instruction data. We release all the models as follows:
* **CodeGen2.5-7B-multi** (this repo): Trained on StarCoderData. Licensed under Apache-2.0.
* **CodeGen2.5-7B-mono**: Further trained on additional Python tokens. Licensed under Apache-2.0.
* **CodeGen2.5-7B-instruct**: Further trained from CodeGen2.5-7B-mono on instruction data. *Research purposes only*.
## How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality.
### Pre-requisite
Please install OpenAI `tiktoken` for the tokenizer.
```bash
pip install tiktoken==0.4.0
```
### Causal sampling (code autocompletion)
For regular causal sampling, simply generate completions given the context:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-multi", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-multi")
text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
### Infill sampling
For **infill** sampling, we follow the CodeGen2 format:
* `<mask_N>`: N-th span to be masked. In practice, use `<mask_1>` to where you want to sample infill.
* `<sep>`: Separator token between the suffix and the infilled sample. See below.
* `<eom>`: "End-Of-Mask" token that model will output at the end of infilling. You may use this token to truncate the output.
For example, if we want to generate infill for the following cursor position of a function:
```python
def hello_world():
|
return name
```
we construct an input to the model by
1. Inserting `<mask_1>` token in place of cursor position
2. Append `<sep>` token to indicate the boundary
3. Insert another `<mask_1>` to indicate which mask we want to infill.
The final snippet looks as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-multi", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-multi")
def format(prefix, suffix):
return prefix + "<mask_1>" + suffix + "<|endoftext|>" + "<sep>" + "<mask_1>"
prefix = "def hello_world():\n "
suffix = " return name"
text = format(prefix, suffix)
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=False)[len(text):])
```
You might want to truncate the model output with `<eom>`.
## Evaluation results
We evaluate our models on HumanEval and HumanEval-Infill.
Please refer to the [blog](https://blog.salesforceairesearch.com/codegen25) for more details.
## Intended use and limitations
As an autoregressive language model, CodeGen2.5 is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
## Attribution & Other Requirements
The pretraining dataset of the model was filtered for permissive licenses only.
Nevertheless, the model can generate source code verbatim from the dataset.
The code's license might require attribution and/or other specific requirements that must be respected.
The data provider BigCode provides a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that lets you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
## BibTeX entry and citation info
Please cite CodeGen2 paper:
```bibtex
@article{Nijkamp2023codegen2,
title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
journal={arXiv preprint},
year={2023}
}
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | sokada/codegen25-7b-multi-gguf-with-dummy-tokenizer | [
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LeoMito/chris | LeoMito | 2023-11-29T13:33:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:33:30Z | 2023-11-29T13:13:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LeoMito/chris | [
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douglasrolins/bert-base-multilingual-cased_ft-multilple-choice-enem-ptbr | douglasrolins | 2023-11-29T13:14:47Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:bert-base-multilingual-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:14:47Z | 2023-11-29T13:14:16.000Z | null | null | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-cased_ft-multilple-choice-enem-ptbr
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. -->
# bert-base-multilingual-cased_ft-multilple-choice-enem-ptbr
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.2064
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 467 | 1.6094 | 0.2440 |
| 1.6149 | 2.0 | 934 | 1.6094 | 0.1930 |
| 1.6242 | 3.0 | 1401 | 1.6094 | 0.2064 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | douglasrolins/bert-base-multilingual-cased_ft-multilple-choice-enem-ptbr | [
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golesheed/whisper-adult-7-dutch | golesheed | 2023-11-29T13:14:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:14:31Z | 2023-11-29T13:14:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | golesheed/whisper-adult-7-dutch | [
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minatonamikage/modelsm5 | minatonamikage | 2023-11-29T13:17:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T13:17:03Z | 2023-11-29T13:17:03.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | minatonamikage/modelsm5 | [
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PDWork/notorix-series | PDWork | 2023-11-29T13:17:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:17:14Z | 2023-11-29T13:17:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | PDWork/notorix-series | [
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Realgon/roberta_sst2_padding90model | Realgon | 2023-11-29T14:39:43Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:39:43Z | 2023-11-29T13:18:43.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding90model
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_sst2_padding90model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5344
- Accuracy: 0.9445
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2083 | 0.9171 |
| 0.3231 | 2.0 | 866 | 0.2668 | 0.9308 |
| 0.1721 | 3.0 | 1299 | 0.3085 | 0.9303 |
| 0.1148 | 4.0 | 1732 | 0.2910 | 0.9363 |
| 0.0679 | 5.0 | 2165 | 0.4164 | 0.9325 |
| 0.0424 | 6.0 | 2598 | 0.3419 | 0.9489 |
| 0.0292 | 7.0 | 3031 | 0.4437 | 0.9418 |
| 0.0292 | 8.0 | 3464 | 0.6145 | 0.9171 |
| 0.023 | 9.0 | 3897 | 0.4383 | 0.9456 |
| 0.0181 | 10.0 | 4330 | 0.4449 | 0.9462 |
| 0.0102 | 11.0 | 4763 | 0.6482 | 0.9270 |
| 0.0131 | 12.0 | 5196 | 0.4929 | 0.9418 |
| 0.0053 | 13.0 | 5629 | 0.4902 | 0.9473 |
| 0.0065 | 14.0 | 6062 | 0.4971 | 0.9434 |
| 0.0065 | 15.0 | 6495 | 0.5722 | 0.9385 |
| 0.0071 | 16.0 | 6928 | 0.5055 | 0.9445 |
| 0.0057 | 17.0 | 7361 | 0.5643 | 0.9418 |
| 0.0033 | 18.0 | 7794 | 0.5291 | 0.9412 |
| 0.0053 | 19.0 | 8227 | 0.5127 | 0.9473 |
| 0.0006 | 20.0 | 8660 | 0.5344 | 0.9445 |
### 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_sst2_padding90model | [
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AllanOuii/zephyr-ikomia-chatbot_v1 | AllanOuii | 2023-11-29T13:36:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:36:30Z | 2023-11-29T13:20:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AllanOuii/zephyr-ikomia-chatbot_v1 | [
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arpandebnath/result | arpandebnath | 2023-11-29T13:20:05Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:20:05Z | 2023-11-29T13:20:05.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | arpandebnath/result | [
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rupeshs/sdxl-turbo-openvino-int8 | rupeshs | 2023-11-29T13:45:00Z | 0 | 1 | null | [
"stablediffusion",
"openvino",
"add",
"text-to-image",
"en",
"region:us"
] | 2023-11-29T13:45:00Z | 2023-11-29T13:20:16.000Z | null | null | ---
language:
- en
pipeline_tag: text-to-image
tags:
- stablediffusion
- openvino
- add
---
The SDXL Turbo model is converted to OpenVINO for the fast inference on CPU. This model is intended for research purpose only.
Original Model : [sdxl-turbo](https://huggingface.co/stabilityai/sdxl-turbo)
The SDXL Turbo Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.
| null | null | text-to-image | null | null | null | null | null | null | null | null | null | rupeshs/sdxl-turbo-openvino-int8 | [
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ThanhMai/ddpm-clips | ThanhMai | 2023-11-29T13:38:28Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T13:38:28Z | 2023-11-29T13:20:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ThanhMai/ddpm-clips | [
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Luckyluuuc/taxi-v3 | Luckyluuuc | 2023-11-29T13:21:36Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T13:21:36Z | 2023-11-29T13:21:34.000Z | 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="Luckyluuuc/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 | Luckyluuuc/taxi-v3 | [
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minatonamikage/my_awesome_qa_model | minatonamikage | 2023-11-29T13:23:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:23:37Z | 2023-11-29T13:23:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | minatonamikage/my_awesome_qa_model | [
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tom192180/distilbert-base-uncased_tvl_zephyr_1shot | tom192180 | 2023-11-29T13:44:19Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:44:19Z | 2023-11-29T13:24:17.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/distilbert-base-uncased_tvl_zephyr_1shot | [
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Vishal24/keyword_brand_cat_adapter_v1 | Vishal24 | 2023-11-29T13:24:34Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | 2023-11-29T13:24:34Z | 2023-11-29T13:24:23.000Z | null | null | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# 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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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
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anetakahle/OpenChat3_5-km-finetuned | anetakahle | 2023-11-29T13:25:39Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T13:25:39Z | 2023-11-29T13:25:39.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | anetakahle/OpenChat3_5-km-finetuned | [
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TheBloke/tigerbot-70B-chat-v2-GGUF | TheBloke | 2023-11-29T23:54:11Z | 0 | 1 | null | [
"transformers",
"gguf",
"llama",
"zh",
"en",
"base_model:TigerResearch/tigerbot-70b-chat-v2",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:54:11Z | 2023-11-29T13:25:44.000Z | null | null | ---
base_model: TigerResearch/tigerbot-70b-chat-v2
inference: false
language:
- zh
- en
license: apache-2.0
model_creator: Tiger Research
model_name: Tigerbot 70B Chat V2
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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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Tigerbot 70B Chat V2 - GGUF
- Model creator: [Tiger Research](https://huggingface.co/TigerResearch)
- Original model: [Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Tiger Research's Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2).
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/tigerbot-70B-chat-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF)
* [Tiger Research's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2)
<!-- 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 `apache-2.0`, 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: [Tiger Research's Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [tigerbot-70b-chat-v2.Q2_K.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q2_K.gguf) | Q2_K | 2 | 29.55 GB| 32.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [tigerbot-70b-chat-v2.Q3_K_S.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q3_K_S.gguf) | Q3_K_S | 3 | 30.21 GB| 32.71 GB | very small, high quality loss |
| [tigerbot-70b-chat-v2.Q3_K_M.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q3_K_M.gguf) | Q3_K_M | 3 | 33.48 GB| 35.98 GB | very small, high quality loss |
| [tigerbot-70b-chat-v2.Q3_K_L.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q3_K_L.gguf) | Q3_K_L | 3 | 36.44 GB| 38.94 GB | small, substantial quality loss |
| [tigerbot-70b-chat-v2.Q4_0.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q4_0.gguf) | Q4_0 | 4 | 39.20 GB| 41.70 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [tigerbot-70b-chat-v2.Q4_K_S.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q4_K_S.gguf) | Q4_K_S | 4 | 39.40 GB| 41.90 GB | small, greater quality loss |
| [tigerbot-70b-chat-v2.Q4_K_M.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q4_K_M.gguf) | Q4_K_M | 4 | 41.75 GB| 44.25 GB | medium, balanced quality - recommended |
| [tigerbot-70b-chat-v2.Q5_0.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q5_0.gguf) | Q5_0 | 5 | 47.81 GB| 50.31 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [tigerbot-70b-chat-v2.Q5_K_S.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q5_K_S.gguf) | Q5_K_S | 5 | 47.81 GB| 50.31 GB | large, low quality loss - recommended |
| [tigerbot-70b-chat-v2.Q5_K_M.gguf](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF/blob/main/tigerbot-70b-chat-v2.Q5_K_M.gguf) | Q5_K_M | 5 | 49.11 GB| 51.61 GB | large, very low quality loss - recommended |
| tigerbot-70b-chat-v2.Q6_K.gguf | Q6_K | 6 | 56.97 GB| 59.47 GB | very large, extremely low quality loss |
| tigerbot-70b-chat-v2.Q8_0.gguf | Q8_0 | 8 | 73.79 GB| 76.29 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.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `tigerbot-70b-chat-v2.Q6_K.gguf-split-a`
* `tigerbot-70b-chat-v2.Q6_K.gguf-split-b`
### q8_0
Please download:
* `tigerbot-70b-chat-v2.Q8_0.gguf-split-a`
* `tigerbot-70b-chat-v2.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat tigerbot-70b-chat-v2.Q6_K.gguf-split-* > tigerbot-70b-chat-v2.Q6_K.gguf && rm tigerbot-70b-chat-v2.Q6_K.gguf-split-*
cat tigerbot-70b-chat-v2.Q8_0.gguf-split-* > tigerbot-70b-chat-v2.Q8_0.gguf && rm tigerbot-70b-chat-v2.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B tigerbot-70b-chat-v2.Q6_K.gguf-split-a + tigerbot-70b-chat-v2.Q6_K.gguf-split-b tigerbot-70b-chat-v2.Q6_K.gguf
del tigerbot-70b-chat-v2.Q6_K.gguf-split-a tigerbot-70b-chat-v2.Q6_K.gguf-split-b
COPY /B tigerbot-70b-chat-v2.Q8_0.gguf-split-a + tigerbot-70b-chat-v2.Q8_0.gguf-split-b tigerbot-70b-chat-v2.Q8_0.gguf
del tigerbot-70b-chat-v2.Q8_0.gguf-split-a tigerbot-70b-chat-v2.Q8_0.gguf-split-b
```
</details>
<!-- 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/tigerbot-70B-chat-v2-GGUF and below it, a specific filename to download, such as: tigerbot-70b-chat-v2.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/tigerbot-70B-chat-v2-GGUF tigerbot-70b-chat-v2.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/tigerbot-70B-chat-v2-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/tigerbot-70B-chat-v2-GGUF tigerbot-70b-chat-v2.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 tigerbot-70b-chat-v2.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` 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="./tigerbot-70b-chat-v2.Q4_K_M.gguf", # Download the model file first
n_ctx=2048, # 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(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # 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="./tigerbot-70b-chat-v2.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: Tiger Research's Tigerbot 70B Chat V2
<div style="width: 100%;">
<p align="center" width="20%">
<img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" width="20%", style="display: block; margin: auto;"></img>
</p>
</div>
<p align="center">
<font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font>
</p>
<p align="center">
💻<a href="https://github.com/TigerResearch/TigerBot" target="_blank">Github</a> • 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>
# 快速开始
- 方法1，通过transformers使用
- 下载 TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- 启动infer代码
```shell
python infer.py --model_path TigerResearch/tigerbot-70b-chat-v2
```
- 方法2:
- 下载 TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- 安装git lfs： `git lfs install`
- 通过huggingface或modelscope平台下载权重
```shell
git clone https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-chat-v2.git
```
- 启动infer代码
```shell
python infer.py --model_path tigerbot-70b-chat-v2
```
------
# Quick Start
- Method 1, use through transformers
- Clone TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- Run infer script
```shell
python infer.py --model_path TigerResearch/tigerbot-70b-chat-v2
```
- Method 2:
- Clone TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- install git lfs： `git lfs install`
- Download weights from huggingface or modelscope
```shell
git clone https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-chat-v2.git
```
- Run infer script
```shell
python infer.py --model_path tigerbot-70b-chat-v2
```
<!-- original-model-card end -->
| null | transformers | null | null | null | null | null | null | null | null | null | null | TheBloke/tigerbot-70B-chat-v2-GGUF | [
-0.6243873834609985,
-0.8766554594039917,
0.2917075753211975,
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TheBloke/tigerbot-70B-chat-v2-AWQ | TheBloke | 2023-11-29T13:25:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:25:44Z | 2023-11-29T13:25:44.000Z | null | null | ---
base_model: TigerResearch/tigerbot-70b-chat-v2
inference: false
language:
- zh
- en
license: apache-2.0
model_creator: Tiger Research
model_name: Tigerbot 70B Chat V2
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 -->
<!-- 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 -->
# Tigerbot 70B Chat V2 - AWQ
- Model creator: [Tiger Research](https://huggingface.co/TigerResearch)
- Original model: [Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2)
<!-- description start -->
## Description
This repo contains AWQ model files for [Tiger Research's Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2).
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/tigerbot-70B-chat-v2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/tigerbot-70B-chat-v2-GGUF)
* [Tiger Research's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2)
<!-- 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 `apache-2.0`, 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: [Tiger Research's Tigerbot 70B Chat V2](https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2).
<!-- licensing 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/tigerbot-70B-chat-v2-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 37.55 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/tigerbot-70B-chat-v2-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: `tigerbot-70B-chat-v2-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/tigerbot-70B-chat-v2-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/tigerbot-70B-chat-v2-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/tigerbot-70B-chat-v2-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'''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_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/tigerbot-70B-chat-v2-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# 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: Tiger Research's Tigerbot 70B Chat V2
<div style="width: 100%;">
<p align="center" width="20%">
<img src="http://x-pai.algolet.com/bot/img/logo_core.png" alt="TigerBot" width="20%", style="display: block; margin: auto;"></img>
</p>
</div>
<p align="center">
<font face="黑体" size=5"> A cutting-edge foundation for your very own LLM. </font>
</p>
<p align="center">
💻<a href="https://github.com/TigerResearch/TigerBot" target="_blank">Github</a> • 🌐 <a href="https://tigerbot.com/" target="_blank">TigerBot</a> • 🤗 <a href="https://huggingface.co/TigerResearch" target="_blank">Hugging Face</a>
</p>
# 快速开始
- 方法1，通过transformers使用
- 下载 TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- 启动infer代码
```shell
python infer.py --model_path TigerResearch/tigerbot-70b-chat-v2
```
- 方法2:
- 下载 TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- 安装git lfs： `git lfs install`
- 通过huggingface或modelscope平台下载权重
```shell
git clone https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-chat-v2.git
```
- 启动infer代码
```shell
python infer.py --model_path tigerbot-70b-chat-v2
```
------
# Quick Start
- Method 1, use through transformers
- Clone TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- Run infer script
```shell
python infer.py --model_path TigerResearch/tigerbot-70b-chat-v2
```
- Method 2:
- Clone TigerBot Repo
```shell
git clone https://github.com/TigerResearch/TigerBot.git
```
- install git lfs： `git lfs install`
- Download weights from huggingface or modelscope
```shell
git clone https://huggingface.co/TigerResearch/tigerbot-70b-chat-v2
git clone https://www.modelscope.cn/TigerResearch/tigerbot-70b-chat-v2.git
```
- Run infer script
```shell
python infer.py --model_path tigerbot-70b-chat-v2
```
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tom192180/distilbert-base-uncased_tvl_zephyr_3shot | tom192180 | 2023-11-29T13:41:42Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:41:42Z | 2023-11-29T13:26:25.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/distilbert-base-uncased_tvl_zephyr_3shot | [
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ErnestBeckham/mistral-finetuned-samsum | ErnestBeckham | 2023-11-29T14:20:17Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:20:17Z | 2023-11-29T13:29:14.000Z | null | null | ---
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: mistral-finetuned-samsum
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. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | ErnestBeckham/mistral-finetuned-samsum | [
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tom192180/distilbert-base-uncased_tvl_gd | tom192180 | 2023-11-29T13:39:00Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:39:00Z | 2023-11-29T13:29:39.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/distilbert-base-uncased_tvl_gd | [
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komrade13/Cat-Breed-Classifier | komrade13 | 2023-11-29T13:37:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:37:54Z | 2023-11-29T13:37:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | komrade13/Cat-Breed-Classifier | [
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cfs1/llama-2-7b-chat-testlll | cfs1 | 2023-11-29T13:39:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:39:55Z | 2023-11-29T13:39:54.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | cfs1/llama-2-7b-chat-testlll | [
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SaladSlayer00/Prov_SML | SaladSlayer00 | 2023-11-29T16:08:03Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:08:03Z | 2023-11-29T13:40:33.000Z | null | null | ---
title: Prov SML
emoji: 😻
colorFrom: pink
colorTo: yellow
sdk: gradio
sdk_version: 4.7.1
app_file: app.py
pinned: false
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | SaladSlayer00/Prov_SML | [
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Liberty-L/Multiple_Choice_swag_lr | Liberty-L | 2023-11-29T13:41:04Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:Liberty-L/swag_pretrained",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:41:04Z | 2023-11-29T13:40:37.000Z | null | null | ---
base_model: Liberty-L/swag_pretrained
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Multiple_Choice_swag_lr
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. -->
# Multiple_Choice_swag_lr
This model is a fine-tuned version of [Liberty-L/swag_pretrained](https://huggingface.co/Liberty-L/swag_pretrained) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6283
- Accuracy: 0.6483
## 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: 7e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1115 | 1.0 | 1271 | 1.0120 | 0.5954 |
| 0.7999 | 2.0 | 2542 | 1.0345 | 0.6267 |
| 0.4649 | 3.0 | 3813 | 1.2207 | 0.6428 |
| 0.2354 | 4.0 | 5084 | 1.6283 | 0.6483 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | Liberty-L/Multiple_Choice_swag_lr | [
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mjcarleb/llama_7B_priscilla-fine-tuned.gguf | mjcarleb | 2023-11-29T13:49:24Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T13:49:24Z | 2023-11-29T13:46:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mjcarleb/llama_7B_priscilla-fine-tuned.gguf | [
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SamGimbel98/T5_MoleculeTranslator | SamGimbel98 | 2023-11-29T13:47:47Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T13:47:47Z | 2023-11-29T13:47:23.000Z | null | null | ---
license: mit
--- | null | null | null | null | null | null | null | null | null | null | null | null | SamGimbel98/T5_MoleculeTranslator | [
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MaxT/poem_sentiment | MaxT | 2023-11-29T13:59:29Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:poem_sentiment",
"base_model:roberta-base",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:59:29Z | 2023-11-29T13:47:43.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- poem_sentiment
metrics:
- accuracy
model-index:
- name: poem_sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: poem_sentiment
type: poem_sentiment
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8857142857142857
---
<!-- 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. -->
# poem_sentiment
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the poem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- 0: {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19}
- 1: {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17}
- 2: {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69}
- Accuracy: 0.8857
- Macro avg: {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105}
- Weighted avg: {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105}
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | 0 | 1 | 2 | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|
| 1.0922 | 1.0 | 112 | 0.8825 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 19} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 17} | {'precision': 0.6571428571428571, 'recall': 1.0, 'f1-score': 0.7931034482758621, 'support': 69} | 0.6571 | {'precision': 0.21904761904761905, 'recall': 0.3333333333333333, 'f1-score': 0.26436781609195403, 'support': 105} | {'precision': 0.43183673469387757, 'recall': 0.6571428571428571, 'f1-score': 0.5211822660098522, 'support': 105} |
| 0.6877 | 2.0 | 224 | 0.4747 | {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19} | {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17} | {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69} | 0.8857 | {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105} | {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105} |
| 0.5299 | 3.0 | 336 | 0.6595 | {'precision': 0.8, 'recall': 0.8421052631578947, 'f1-score': 0.8205128205128205, 'support': 19} | {'precision': 1.0, 'recall': 0.4117647058823529, 'f1-score': 0.5833333333333334, 'support': 17} | {'precision': 0.8461538461538461, 'recall': 0.9565217391304348, 'f1-score': 0.8979591836734695, 'support': 69} | 0.8476 | {'precision': 0.882051282051282, 'recall': 0.7367972360568942, 'f1-score': 0.7672684458398744, 'support': 105} | {'precision': 0.8627106227106227, 'recall': 0.8476190476190476, 'f1-score': 0.8330056564750442, 'support': 105} |
| 0.9027 | 4.0 | 448 | 0.5981 | {'precision': 1.0, 'recall': 0.7368421052631579, 'f1-score': 0.8484848484848484, 'support': 19} | {'precision': 0.7333333333333333, 'recall': 0.6470588235294118, 'f1-score': 0.6875, 'support': 17} | {'precision': 0.868421052631579, 'recall': 0.9565217391304348, 'f1-score': 0.9103448275862069, 'support': 69} | 0.8667 | {'precision': 0.867251461988304, 'recall': 0.7801408893076681, 'f1-score': 0.8154432253570185, 'support': 105} | {'precision': 0.870359231411863, 'recall': 0.8666666666666667, 'f1-score': 0.863071478330099, 'support': 105} |
| 0.4588 | 5.0 | 560 | 0.7815 | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1-score': 0.8292682926829269, 'support': 19} | {'precision': 0.6470588235294118, 'recall': 0.6470588235294118, 'f1-score': 0.6470588235294118, 'support': 17} | {'precision': 0.8939393939393939, 'recall': 0.855072463768116, 'f1-score': 0.8740740740740741, 'support': 69} | 0.8286 | {'precision': 0.7712418300653595, 'recall': 0.7989560431342637, 'f1-score': 0.7834670634288043, 'support': 105} | {'precision': 0.832034632034632, 'recall': 0.8285714285714286, 'f1-score': 0.8292115111627308, 'support': 105} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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zsy12345/model-medium-Punjabi-4-0-4000-1e-05-8-8-1000 | zsy12345 | 2023-11-29T13:48:37Z | 0 | 0 | null | [
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] | 2023-11-29T13:48:37Z | 2023-11-29T13:48:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | zsy12345/model-medium-Punjabi-4-0-4000-1e-05-8-8-1000 | [
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yahup/cicek | yahup | 2023-11-29T13:49:28Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T13:49:28Z | 2023-11-29T13:49:28.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | yahup/cicek | [
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RishuD7/CML_Text_v1 | RishuD7 | 2023-11-29T13:49:40Z | 0 | 0 | null | [
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SakuraLLM/Sakura-13B-LNovel-v0.9.0pre1 | SakuraLLM | 2023-11-29T15:31:11Z | 0 | 0 | null | [
"transformers",
"pytorch",
"qwen",
"text-generation",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | 2023-11-29T15:31:11Z | 2023-11-29T13:49:59.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | SakuraLLM/Sakura-13B-LNovel-v0.9.0pre1 | [
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zzjo/whisper_medium | zzjo | 2023-11-29T13:52:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:52:49Z | 2023-11-29T13:52:49.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | zzjo/whisper_medium | [
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LoneStriker/psyonic-cetacean-20B-4.0bpw-h6-exl2 | LoneStriker | 2023-11-29T15:36:43Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:36:43Z | 2023-11-29T13:53:24.000Z | null | null | ---
license: other
license_name: microsoft-research-license
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/psyonic-cetacean-20B-4.0bpw-h6-exl2 | [
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maidacundo/my_awesome_peft_model | maidacundo | 2023-11-29T13:53:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:53:40Z | 2023-11-29T13:53:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | maidacundo/my_awesome_peft_model | [
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maff666/Maffnormal_1 | maff666 | 2023-11-29T13:54:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:54:01Z | 2023-11-29T13:54:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | maff666/Maffnormal_1 | [
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cgfgui/SDXL | cgfgui | 2023-11-29T13:54:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:54:38Z | 2023-11-29T13:54:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | cgfgui/SDXL | [
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makhataei/albert-fa-zwnj-base-v2 | makhataei | 2023-11-29T13:55:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T13:55:52Z | 2023-11-29T13:55:49.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | makhataei/albert-fa-zwnj-base-v2 | [
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TheBloke/deepseek-llm-67b-chat-GGUF | TheBloke | 2023-11-29T14:41:43Z | 0 | 1 | null | [
"transformers",
"gguf",
"deepseek",
"base_model:deepseek-ai/deepseek-llm-67b-chat",
"license:other",
"region:us"
] | 2023-11-29T14:41:43Z | 2023-11-29T13:56:33.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-67b-chat
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek Llm 67B Chat
model_type: deepseek
prompt_template: 'User: {prompt}
Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek Llm 67B Chat - GGUF
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- description start -->
## Description
This repo contains GGUF format model files for [DeepSeek's Deepseek Llm 67B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat).
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/deepseek-llm-67b-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-67b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: DeepSeek-LLM
```
User: {prompt}
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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [deepseek-llm-67b-chat.Q2_K.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q2_K.gguf) | Q2_K | 2 | 28.50 GB| 31.00 GB | smallest, significant quality loss - not recommended for most purposes |
| [deepseek-llm-67b-chat.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q3_K_S.gguf) | Q3_K_S | 3 | 29.31 GB| 31.81 GB | very small, high quality loss |
| [deepseek-llm-67b-chat.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q3_K_M.gguf) | Q3_K_M | 3 | 32.58 GB| 35.08 GB | very small, high quality loss |
| [deepseek-llm-67b-chat.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q3_K_L.gguf) | Q3_K_L | 3 | 35.58 GB| 38.08 GB | small, substantial quality loss |
| [deepseek-llm-67b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q4_0.gguf) | Q4_0 | 4 | 38.15 GB| 40.65 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [deepseek-llm-67b-chat.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q4_K_S.gguf) | Q4_K_S | 4 | 38.25 GB| 40.75 GB | small, greater quality loss |
| [deepseek-llm-67b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q4_K_M.gguf) | Q4_K_M | 4 | 40.44 GB| 42.94 GB | medium, balanced quality - recommended |
| [deepseek-llm-67b-chat.Q5_0.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q5_0.gguf) | Q5_0 | 5 | 46.48 GB| 48.98 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [deepseek-llm-67b-chat.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q5_K_S.gguf) | Q5_K_S | 5 | 46.48 GB| 48.98 GB | large, low quality loss - recommended |
| [deepseek-llm-67b-chat.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepseek-llm-67b-chat-GGUF/blob/main/deepseek-llm-67b-chat.Q5_K_M.gguf) | Q5_K_M | 5 | 47.65 GB| 50.15 GB | large, very low quality loss - recommended |
| deepseek-llm-67b-chat.Q6_K.gguf | Q6_K | 6 | 55.32 GB| 57.82 GB | very large, extremely low quality loss |
| deepseek-llm-67b-chat.Q8_0.gguf | Q8_0 | 8 | 71.65 GB| 74.15 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.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `deepseek-llm-67b-chat.Q6_K.gguf-split-a`
* `deepseek-llm-67b-chat.Q6_K.gguf-split-b`
### q8_0
Please download:
* `deepseek-llm-67b-chat.Q8_0.gguf-split-a`
* `deepseek-llm-67b-chat.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat deepseek-llm-67b-chat.Q6_K.gguf-split-* > deepseek-llm-67b-chat.Q6_K.gguf && rm deepseek-llm-67b-chat.Q6_K.gguf-split-*
cat deepseek-llm-67b-chat.Q8_0.gguf-split-* > deepseek-llm-67b-chat.Q8_0.gguf && rm deepseek-llm-67b-chat.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B deepseek-llm-67b-chat.Q6_K.gguf-split-a + deepseek-llm-67b-chat.Q6_K.gguf-split-b deepseek-llm-67b-chat.Q6_K.gguf
del deepseek-llm-67b-chat.Q6_K.gguf-split-a deepseek-llm-67b-chat.Q6_K.gguf-split-b
COPY /B deepseek-llm-67b-chat.Q8_0.gguf-split-a + deepseek-llm-67b-chat.Q8_0.gguf-split-b deepseek-llm-67b-chat.Q8_0.gguf
del deepseek-llm-67b-chat.Q8_0.gguf-split-a deepseek-llm-67b-chat.Q8_0.gguf-split-b
```
</details>
<!-- 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/deepseek-llm-67b-chat-GGUF and below it, a specific filename to download, such as: deepseek-llm-67b-chat.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/deepseek-llm-67b-chat-GGUF deepseek-llm-67b-chat.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/deepseek-llm-67b-chat-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/deepseek-llm-67b-chat-GGUF deepseek-llm-67b-chat.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 deepseek-llm-67b-chat.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "User: {prompt}\n\nAssistant:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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="./deepseek-llm-67b-chat.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"User: {prompt}\n\nAssistant:", # 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="./deepseek-llm-67b-chat.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: DeepSeek's Deepseek Llm 67B Chat
<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).
<!-- original-model-card end -->
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SIGINT/my_awesome_model | SIGINT | 2023-11-29T14:35:08Z | 0 | 1 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:35:08Z | 2023-11-29T13:57:27.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | SIGINT/my_awesome_model | [
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Mlxa/tuned-flat_shuffle-flat | Mlxa | 2023-11-29T14:01:25Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:01:25Z | 2023-11-29T14:01:23.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mlxa/tuned-flat_shuffle-flat | [
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ersdd/xiaoqingxin | ersdd | 2023-11-29T14:06:21Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:06:21Z | 2023-11-29T14:03:16.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | ersdd/xiaoqingxin | [
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ka05ar/mt0-xl-dravidian-fake-news-classification | ka05ar | 2023-11-29T14:24:28Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:24:28Z | 2023-11-29T14:07:36.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | ka05ar/mt0-xl-dravidian-fake-news-classification | [
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michael-l-a/llm-course | michael-l-a | 2023-11-29T14:07:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:07:40Z | 2023-11-29T14:07:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | michael-l-a/llm-course | [
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Demosthene-OR/t5-small | Demosthene-OR | 2023-11-29T15:11:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:11:04Z | 2023-11-29T14:08:08.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Demosthene-OR/t5-small | [
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zzjo/whisper-medium | zzjo | 2023-11-29T14:09:12Z | 0 | 0 | null | [
"region:us"
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pratap477/test | pratap477 | 2023-11-29T14:12:58Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:12:58Z | 2023-11-29T14:12:58.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pratap477/test | [
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JUJORUME/FT-Spanish-Whisper | JUJORUME | 2023-11-29T14:13:28Z | 0 | 0 | null | [
"doi:10.57967/hf/1404",
"region:us"
] | 2023-11-29T14:13:28Z | 2023-11-29T14:13:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | JUJORUME/FT-Spanish-Whisper | [
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bartowski/deepseek-coder-6.7b-instruct-exl2 | bartowski | 2023-11-29T16:25:28Z | 0 | 0 | null | [
"text-generation",
"license:other",
"region:us"
] | 2023-11-29T16:25:28Z | 2023-11-29T14:14:23.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of deepseek-coder-6.7b-instruct
Using <a href="https://github.com/turboderp/exllamav2/tree/master">turboderp's ExLlamaV2 master</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using Evol-Instruct-Code-80k-v1.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct
<a href="https://huggingface.co/bartowski/deepseek-coder-6.7b-instruct-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-coder-6.7b-instruct-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-coder-6.7b-instruct-exl2/tree/6_0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-coder-6.7b-instruct-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/deepseek-coder-6.7b-instruct-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `deepseek-coder-6.7b-instruct-exl2`:
```shell
mkdir deepseek-coder-6.7b-instruct-exl2
huggingface-cli download bartowski/deepseek-coder-6.7b-instruct-exl2 --local-dir deepseek-coder-6.7b-instruct-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-coder-6.7b-instruct-exl2
huggingface-cli download bartowski/deepseek-coder-6.7b-instruct-exl2 --revision 4_0 --local-dir deepseek-coder-6.7b-instruct-exl2 --local-dir-use-symlinks False
```
| null | null | text-generation | null | null | null | null | null | null | null | null | null | bartowski/deepseek-coder-6.7b-instruct-exl2 | [
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cumbullop/aa | cumbullop | 2023-11-29T14:14:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:14:33Z | 2023-11-29T14:14:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | cumbullop/aa | [
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ersdd/1024xiaoqingxin | ersdd | 2023-11-29T19:23:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T19:23:03Z | 2023-11-29T14:15:11.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | ersdd/1024xiaoqingxin | [
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tom192180/distilbert-base-uncased_tvl_zephyr_7shot | tom192180 | 2023-11-29T14:18:10Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:18:10Z | 2023-11-29T14:16:34.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/distilbert-base-uncased_tvl_zephyr_7shot | [
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mercury131/openchat-cpu-quantization-fork | mercury131 | 2023-11-29T14:16:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:16:35Z | 2023-11-29T14:16:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mercury131/openchat-cpu-quantization-fork | [
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jackbuzz/ko-llama2-finetune | jackbuzz | 2023-11-29T14:26:22Z | 0 | 0 | null | [
"autotrain",
"text-generation",
"region:us"
] | 2023-11-29T14:26:22Z | 2023-11-29T14:16:38.000Z | null | null | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain | null | null | text-generation | null | null | null | null | null | null | null | null | null | jackbuzz/ko-llama2-finetune | [
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DDingcheol/Model-Of-Vqa-And-OkVqa | DDingcheol | 2023-11-29T14:17:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:17:18Z | 2023-11-29T14:17:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DDingcheol/Model-Of-Vqa-And-OkVqa | [
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Englios/q-FrozenLake-v1-4x4-noSlippery | Englios | 2023-11-29T14:25:32Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T14:25:32Z | 2023-11-29T14:18:55.000Z | 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="Englios/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 | Englios/q-FrozenLake-v1-4x4-noSlippery | [
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Giulia1337s/Angyie | Giulia1337s | 2023-11-29T14:18:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:18:55Z | 2023-11-29T14:18:55.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Giulia1337s/Angyie | [
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ludis/tsukasa-120b-qlora | ludis | 2023-11-29T15:16:51Z | 0 | 1 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:PygmalionAI/PIPPA",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:16:51Z | 2023-11-29T14:19:38.000Z | null | null | ---
datasets:
- PygmalionAI/PIPPA
---
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 8x nvidia a100 gpu cluster.
the a100 GPU cluster has been graciously provided by [lloorree](https://huggingface.co/lloorree).
rank 8 qlora (all modules) tune
base model alpindale/goliath-120b tuned on koishi commit 6e675d1 for one epoch
then tuned on pippa 6412b0c for one epoch (metharme completion)
then tuned on limarp (without ponyville, lolicit, all the fallen, and eka's portal subsets) Version 2023-10-19 for 2 epochs in metharme completion format
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ludis/tsukasa-120b-qlora | [
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TheBloke/Iambe-20B-DARE-GGUF | TheBloke | 2023-11-29T22:06:31Z | 0 | 0 | null | [
"transformers",
"gguf",
"llama",
"base_model:athirdpath/Iambe-20b-DARE",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:06:31Z | 2023-11-29T14:20:38.000Z | 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 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
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</div>
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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 -->
# Iambe 20B Dare - GGUF
- 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 GGUF format model files for [LadyBabyloin's Iambe 20B Dare](https://huggingface.co/athirdpath/Iambe-20b-DARE).
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/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 -->
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [iambe-20b-dare.Q2_K.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [iambe-20b-dare.Q3_K_S.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [iambe-20b-dare.Q3_K_M.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [iambe-20b-dare.Q3_K_L.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [iambe-20b-dare.Q4_0.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [iambe-20b-dare.Q4_K_S.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [iambe-20b-dare.Q4_K_M.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [iambe-20b-dare.Q5_0.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [iambe-20b-dare.Q5_K_S.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [iambe-20b-dare.Q5_K_M.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [iambe-20b-dare.Q6_K.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [iambe-20b-dare.Q8_0.gguf](https://huggingface.co/TheBloke/Iambe-20B-DARE-GGUF/blob/main/iambe-20b-dare.Q8_0.gguf) | Q8_0 | 8 | 21.25 GB| 23.75 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/Iambe-20B-DARE-GGUF and below it, a specific filename to download, such as: iambe-20b-dare.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/Iambe-20B-DARE-GGUF iambe-20b-dare.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/Iambe-20B-DARE-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/Iambe-20B-DARE-GGUF iambe-20b-dare.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 iambe-20b-dare.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` 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="./iambe-20b-dare.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # 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(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # 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="./iambe-20b-dare.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: 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.
<!-- original-model-card end -->
| null | transformers | null | null | null | null | null | null | null | null | null | null | TheBloke/Iambe-20B-DARE-GGUF | [
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dmmagdal/flan-t5-small-onnx | dmmagdal | 2023-11-29T14:24:52Z | 0 | 0 | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:24:52Z | 2023-11-29T14:21:09.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | dmmagdal/flan-t5-small-onnx | [
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auberonkuo/dummy-model | auberonkuo | 2023-11-29T14:44:50Z | 0 | 0 | null | [
"adapter-transformers",
"finance",
"text-classification",
"aa",
"dataset:HuggingFaceH4/no_robots",
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:44:50Z | 2023-11-29T14:23:23.000Z | null | null | ---
license: apache-2.0
datasets:
- HuggingFaceH4/no_robots
language:
- aa
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: text-classification
tags:
- finance
--- | null | adapter-transformers | text-classification | null | null | null | null | null | null | null | null | null | auberonkuo/dummy-model | [
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tina87/simcse_retrain_edos_100k_128batch | tina87 | 2023-11-29T14:23:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:23:56Z | 2023-11-29T14:23:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | tina87/simcse_retrain_edos_100k_128batch | [
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LoneStriker/psyonic-cetacean-20B-5.0bpw-h6-exl2 | LoneStriker | 2023-11-29T15:37:06Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:37:06Z | 2023-11-29T14:24:14.000Z | null | null | ---
license: other
license_name: microsoft-research-license
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/psyonic-cetacean-20B-5.0bpw-h6-exl2 | [
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dmmagdal/flan-t5-large-onnx | dmmagdal | 2023-11-29T18:15:23Z | 0 | 0 | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T18:15:23Z | 2023-11-29T14:26:35.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | dmmagdal/flan-t5-large-onnx | [
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shius/plantbert-vanilla-cased | shius | 2023-11-29T15:14:20Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"biology",
"en",
"dataset:shius/plantbert-vanilla-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:14:20Z | 2023-11-29T14:27:00.000Z | null | null | ---
license: mit
datasets:
- shius/plantbert-vanilla-cased
language:
- en
tags:
- biology
---
__Code availability__
- [shiulab/plantbert](https://github.com/ShiuLab/plantbert)
__Relevant settings__
- env:
- rand_seed : 20231002
- test_size : 0.1
- bert_config:
- vocab_size : 30_522
- max_length : 512
- training_arguments:
- resume_from_ckpt : False
- mlm : True
- mlm_prob : 0.2
- eval_strategy : 'steps'
- overwrite_out : True
- num_epochs : 30
- train_batch_size : 20
- grad_acc_steps : 8
- eval_batch_size : 64
- logging_strategy : 'steps'
- log_steps : 500
- save_strategy : 'steps'
- save_steps : 500
- safetensors : True
- load_best_at_end : True
- save_total_limit : 5
__Model evaluation (loss)__
- Training: 1.8376
- Validation: 1.7476 | null | transformers | fill-mask | null | null | null | null | null | null | null | null | null | shius/plantbert-vanilla-cased | [
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Englios/Taxi-v3 | Englios | 2023-11-29T14:30:18Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T14:30:18Z | 2023-11-29T14:30:16.000Z | 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: 7.50 +/- 2.73
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="Englios/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 | Englios/Taxi-v3 | [
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scotta01/model | scotta01 | 2023-11-29T14:30:27Z | 0 | 0 | null | [
"region:us"
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thypogean/my_awesome_wnut_model | thypogean | 2023-11-29T15:06:11Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:06:11Z | 2023-11-29T14:30:31.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | thypogean/my_awesome_wnut_model | [
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ksaisaisai/chatglm_med_lora_2w | ksaisaisai | 2023-11-29T14:30:33Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:30:33Z | 2023-11-29T14:30:33.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | ksaisaisai/chatglm_med_lora_2w | [
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shait7999/MakiNishikino | shait7999 | 2023-11-29T14:38:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:38:38Z | 2023-11-29T14:35:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | shait7999/MakiNishikino | [
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douglasrolins/roberta-large-finetuned-race_ft-multilple-choice-enem | douglasrolins | 2023-11-29T14:36:44Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:LIAMF-USP/roberta-large-finetuned-race",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:36:44Z | 2023-11-29T14:35:58.000Z | null | null | ---
license: mit
base_model: LIAMF-USP/roberta-large-finetuned-race
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-finetuned-race_ft-multilple-choice-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-large-finetuned-race_ft-multilple-choice-enem
This model is a fine-tuned version of [LIAMF-USP/roberta-large-finetuned-race](https://huggingface.co/LIAMF-USP/roberta-large-finetuned-race) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.1984
## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7081 | 1.0 | 933 | 1.6094 | 0.1823 |
| 1.6471 | 2.0 | 1866 | 1.6094 | 0.2064 |
| 1.6607 | 3.0 | 2799 | 1.6094 | 0.1984 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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oliverjthomas2000/2023_11_29-14.34.53python-buffalo-leopard | oliverjthomas2000 | 2023-11-29T14:39:12Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T14:39:12Z | 2023-11-29T14:36:59.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | oliverjthomas2000/2023_11_29-14.34.53python-buffalo-leopard | [
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seonglae/resrer-bart | seonglae | 2023-11-29T21:29:52Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:29:52Z | 2023-11-29T14:38:57.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | seonglae/resrer-bart | [
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glavez1337/ArtcherYT | glavez1337 | 2023-11-29T18:31:45Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T18:31:45Z | 2023-11-29T14:39:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | glavez1337/ArtcherYT | [
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UUUZCX/archmax | UUUZCX | 2023-11-29T14:39:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:39:21Z | 2023-11-29T14:39:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | UUUZCX/archmax | [
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miraksemog/passaro | miraksemog | 2023-11-29T14:39:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:39:25Z | 2023-11-29T14:39:25.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | miraksemog/passaro | [
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Realgon/roberta_sst2_padding100model | Realgon | 2023-11-29T16:06:19Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:06:19Z | 2023-11-29T14:39:47.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding100model
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_sst2_padding100model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4756
- Accuracy: 0.9467
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1945 | 0.9193 |
| 0.347 | 2.0 | 866 | 0.4578 | 0.9121 |
| 0.2005 | 3.0 | 1299 | 0.2934 | 0.9319 |
| 0.1211 | 4.0 | 1732 | 0.3157 | 0.9407 |
| 0.0719 | 5.0 | 2165 | 0.3519 | 0.9396 |
| 0.0425 | 6.0 | 2598 | 0.3947 | 0.9379 |
| 0.028 | 7.0 | 3031 | 0.5409 | 0.9275 |
| 0.028 | 8.0 | 3464 | 0.5252 | 0.9275 |
| 0.0204 | 9.0 | 3897 | 0.4965 | 0.9341 |
| 0.03 | 10.0 | 4330 | 0.4417 | 0.9401 |
| 0.0189 | 11.0 | 4763 | 0.3982 | 0.9429 |
| 0.0146 | 12.0 | 5196 | 0.4508 | 0.9429 |
| 0.0075 | 13.0 | 5629 | 0.4806 | 0.9407 |
| 0.0106 | 14.0 | 6062 | 0.4549 | 0.9456 |
| 0.0106 | 15.0 | 6495 | 0.4606 | 0.9478 |
| 0.0072 | 16.0 | 6928 | 0.5123 | 0.9407 |
| 0.0071 | 17.0 | 7361 | 0.4453 | 0.9500 |
| 0.0017 | 18.0 | 7794 | 0.5241 | 0.9440 |
| 0.0023 | 19.0 | 8227 | 0.4587 | 0.9473 |
| 0.0017 | 20.0 | 8660 | 0.4756 | 0.9467 |
### 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_sst2_padding100model | [
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LoneStriker/psyonic-cetacean-20B-6.0bpw-h6-exl2 | LoneStriker | 2023-11-29T15:37:19Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:37:19Z | 2023-11-29T14:39:47.000Z | null | null | ---
license: other
license_name: microsoft-research-license
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/psyonic-cetacean-20B-6.0bpw-h6-exl2 | [
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dmmagdal/flan-t5-xl-onnx | dmmagdal | 2023-11-29T19:48:12Z | 0 | 0 | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T19:48:12Z | 2023-11-29T14:43:14.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | dmmagdal/flan-t5-xl-onnx | [
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dmmagdal/flan-t5-xxl-onnx | dmmagdal | 2023-11-29T14:43:26Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:43:26Z | 2023-11-29T14:43:26.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | dmmagdal/flan-t5-xxl-onnx | [
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Ryukomatoi/90zz | Ryukomatoi | 2023-11-29T15:20:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:20:54Z | 2023-11-29T14:43:36.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Ryukomatoi/90zz | [
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yanneta2/vit-finetuned-lora-food101 | yanneta2 | 2023-11-29T14:44:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:44:30Z | 2023-11-29T14:44:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | yanneta2/vit-finetuned-lora-food101 | [
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ghassenhannachi/speecht5_finetuned_voxpopuli_lt | ghassenhannachi | 2023-11-29T15:39:33Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:39:33Z | 2023-11-29T14:45:27.000Z | null | null | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_lt
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. -->
# speecht5_finetuned_voxpopuli_lt
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4991
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6111 | 3.85 | 100 | 0.5507 |
| 0.6052 | 7.69 | 200 | 0.5203 |
| 0.5447 | 11.54 | 300 | 0.5029 |
| 0.5584 | 15.38 | 400 | 0.5015 |
| 0.5522 | 19.23 | 500 | 0.4991 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-to-speech | null | null | null | null | null | null | null | null | null | ghassenhannachi/speecht5_finetuned_voxpopuli_lt | [
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arolfflora/deberta-v3-base-finetuned-swag | arolfflora | 2023-11-30T00:40:08Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:40:08Z | 2023-11-29T14:45:41.000Z | null | null | Entry not found | null | transformers | multiple-choice | null | null | null | null | null | null | null | null | null | arolfflora/deberta-v3-base-finetuned-swag | [
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Aleksia/finetuning-distilBert_sentiment_v2 | Aleksia | 2023-11-29T14:56:15Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:56:15Z | 2023-11-29T14:50:16.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Aleksia/finetuning-distilBert_sentiment_v2 | [
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Gabagoo/ddpm-butterflies-128 | Gabagoo | 2023-11-29T14:52:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:52:06Z | 2023-11-29T14:52:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Gabagoo/ddpm-butterflies-128 | [
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seonglae/resrer-bart-sep | seonglae | 2023-11-29T21:30:08Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:30:08Z | 2023-11-29T14:54:26.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | seonglae/resrer-bart-sep | [
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