Search is not available for this dataset
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null | peft | ## 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 0.4.0
| {"library_name": "peft"} | chakkakrishna/llamareqa | null | [
"peft",
"safetensors",
"llama",
"region:us"
]
| null | 2024-04-25T18:07:59+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | 1024m/MISTRAL7B-01-EXALT1A-16bit | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:08:16+00:00 |
null | null | https://civitai.com/models/417259/alisa-mikhailovna-kujou-ayra-san-or-my-deskmate-alya-sometimes-hides-her-feelings-in-russian-or-tokidoki-bosotto-roshia-go-de-dereru-tonari-no-arya-san | {"license": "creativeml-openrail-m"} | LarryAIDraw/Arya-06 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:09:37+00:00 |
null | null | {} | Umbrosov/Orihime | null | [
"region:us"
]
| null | 2024-04-25T18:09:56+00:00 |
|
text-generation | null |
# Mistral 7B Instruct v0.2 - GGUF
- Model creator: [Mistral AI_](https://huggingface.co/mistralai)
- Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
<!-- 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 -->
<!-- prompt-template start -->
## Prompt template: Mistral
```
<s>[INST] {prompt} [/INST]
```
<!-- prompt-template end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mistral-7b-instruct-v0.2.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mistral-7b-instruct-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [mistral-7b-instruct-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mistral-7b-instruct-v0.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mistral-7b-instruct-v0.2.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mistral-7b-instruct-v0.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mistral-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mistral-7b-instruct-v0.2.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mistral-7b-instruct-v0.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mistral-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mistral-7b-instruct-v0.2.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mistral-7b-instruct-v0.2.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Instruct-v0.2-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.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/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.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/Mistral-7B-Instruct-v0.2-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/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.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 mistral-7b-instruct-v0.2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## 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="./mistral-7b-instruct-v0.2.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mistral-7b-instruct-v0.2.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 -->
<!-- original-model-card start -->
# Original model card: Mistral AI_'s Mistral 7B Instruct v0.2
# Model Card for Mistral-7B-Instruct-v0.2
The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
<!-- original-model-card end -->
| {"license": "apache-2.0", "tags": ["finetuned"], "model_name": "Mistral 7B Instruct v0.2", "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "inference": false, "model_creator": "Mistral AI_", "model_type": "mistral", "pipeline_tag": "text-generation", "prompt_template": "<s>[INST] {prompt} [/INST]\n", "quantized_by": "VesperAI"} | VesperAI/Mistral-7B-Instruct-v0.2-gguf | null | [
"gguf",
"finetuned",
"text-generation",
"arxiv:2310.06825",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-25T18:10:05+00:00 |
text-generation | transformers |
# KangalKhan-Alpha-Sapphiroid-7B-Fixed
KangalKhan-Alpha-Sapphiroid-7B-Fixed is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaist-ai/mistral-orpo-capybara-7k](https://huggingface.co/kaist-ai/mistral-orpo-capybara-7k)
* [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: kaist-ai/mistral-orpo-capybara-7k
layer_range: [0, 32]
- model: argilla/CapybaraHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: kaist-ai/mistral-orpo-capybara-7k
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "kaist-ai/mistral-orpo-capybara-7k", "argilla/CapybaraHermes-2.5-Mistral-7B"], "base_model": ["kaist-ai/mistral-orpo-capybara-7k", "argilla/CapybaraHermes-2.5-Mistral-7B"]} | Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"kaist-ai/mistral-orpo-capybara-7k",
"argilla/CapybaraHermes-2.5-Mistral-7B",
"conversational",
"en",
"base_model:kaist-ai/mistral-orpo-capybara-7k",
"base_model:argilla/CapybaraHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:10:06+00:00 |
null | null | https://civitai.com/models/418957/lawine-sousou-no-frieren | {"license": "creativeml-openrail-m"} | LarryAIDraw/Lawine_snf_ | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:10:08+00:00 |
text-generation | transformers |
# Model Card for alokabhishek/Meta-Llama-3-8B-Instruct-GGUF
<!-- Provide a quick summary of what the model is/does. -->
This repo GGUF quantized version of Meta's meta-llama/Meta-Llama-3-8B-Instruct model using llama.cpp.
## Model Details
- Model creator: [Meta](https://huggingface.co/meta-llama)
- Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### About GGUF quantization using llama.cpp
- llama.cpp github repo: [llama.cpp github repo](https://github.com/ggerganov/llama.cpp)
- llama-cpp-python github repo: [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
# How to Get Started with the Model
Use the code below to get started with the model. This code uses llama-cpp-python
```python
import time
import os
import dotenv
import json
import torch
from torch import bfloat16
from llama_cpp import Llama, llama_tokenize, LlamaGrammar
from inference.chat_prompt_format_util import formatted_chat_prompt
from huggingface_hub import login, HfApi
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
pipeline,
)
prompt_instruction = "You are a helpful, and fun loving assistant. Always answer as jestfully as possible."
user_question = "Why is Hulk always Angry?"
chat_messages = [
{"role": "system", "content": str(prompt_instruction)},
{"role": "user", "content": str(user_question)},
]
model_id = "alokabhishek/Meta-Llama-3-8B-Instruct-GGUF"
model_file = "meta-llama-3-8b-instruct.Q4_K_M.gguf"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model_name = Llama.from_pretrained(
repo_id=model_id,
filename=model_file,
verbose=False,
)
terminators = [
"<|end_of_text|>",
"<|eot_id|>",
"assistant\n\n",
]
llm_response = model_name.create_chat_completion(
messages=chat_messages,
max_tokens=1024,
temperature=1,
top_k=50,
top_p=1,
stop=terminators,
)
print("\nllm_response: ", llm_response)
llm_answer = llm_response["choices"][0]["message"]["content"]
print("\nllm_answer: ", llm_answer)
```
## Original Meta's Llama-3 Model Card:
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"license": "other", "library_name": "transformers", "tags": ["GGUF", "llama-3", "llama", "Q4_K_M", "Q5_K_M", "meta", "facebook", "quantized", "8b"], "license_name": "llama3", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | alokabhishek/Meta-Llama-3-8B-Instruct-GGUF | null | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"GGUF",
"llama-3",
"Q4_K_M",
"Q5_K_M",
"meta",
"facebook",
"quantized",
"8b",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:10:14+00:00 |
null | null | https://civitai.com/models/150021/aqua-konosuba-lora | {"license": "creativeml-openrail-m"} | LarryAIDraw/aqua-10 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:10:33+00:00 |
null | null | https://civitai.com/models/150383/megumin-konosuba-lora | {"license": "creativeml-openrail-m"} | LarryAIDraw/megumin-10 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:10:53+00:00 |
null | null | https://civitai.com/models/132043/aqua-konosuba-anime-character | {"license": "creativeml-openrail-m"} | LarryAIDraw/Aqua_Konosuba | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:11:13+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# saiga_task_double_lora350
This model is a fine-tuned version of [TheBloke/Llama-2-7B-fp16](https://huggingface.co/TheBloke/Llama-2-7B-fp16) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6634
## 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: 0.0003
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 350
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5302 | 9.26 | 50 | 2.1777 |
| 1.0061 | 18.52 | 100 | 2.4897 |
| 0.5505 | 27.78 | 150 | 2.8614 |
| 0.2832 | 37.04 | 200 | 3.2451 |
| 0.1525 | 46.3 | 250 | 3.5339 |
| 0.1026 | 55.56 | 300 | 3.6407 |
| 0.0902 | 64.81 | 350 | 3.6634 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.36.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Llama-2-7B-fp16", "model-index": [{"name": "saiga_task_double_lora350", "results": []}]} | marcus2000/saiga_task_double_lora350 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Llama-2-7B-fp16",
"region:us"
]
| null | 2024-04-25T18:12:14+00:00 |
null | null | {} | ee111/anuvjain11 | null | [
"region:us"
]
| null | 2024-04-25T18:12:36+00:00 |
|
text-generation | transformers | {} | laitrongduc/llama-2-7b-miniguanaco | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:13:22+00:00 |
|
null | null | {} | ashishp-wiai/Rice_LoRA_80-2024-04-25 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-25T18:13:29+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Task** WASSA Shared Task 1A 2024**
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
prompt format :
-"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-### Instruction:
-{Given the input text , classify it based on what emotion is being exibited among the following : Joy/Neutral/Anger/Love/Sadness/Fear. Respond with only one emotion only among the options given. Respond with ONLY ONE word and nothing else. }
-### Input:
-{} ( add input text here and remove this text )
-### Response:
-{} ( leave blank and remove this text ) """
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | 1024m/MISTRAL7B-01-EXALT1A-4bit | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-25T18:14:23+00:00 |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "284.80 +/- 18.80", "name": "mean_reward", "verified": false}]}]}]} | Ishan009/LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-25T18:15:38+00:00 |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "tiiuae/falcon-7b"} | ClaudiaIoana550/nou_try7 | null | [
"peft",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b",
"region:us"
]
| null | 2024-04-25T18:15:52+00:00 |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | PablitoGil14/ModelCalzados | null | [
"fastai",
"has_space",
"region:us"
]
| null | 2024-04-25T18:16:09+00:00 |
automatic-speech-recognition | transformers | {} | xeon0618/indic_gujarati | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:16:17+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: NousResearch/Meta-Llama-3-70B
model_type: LlamaForCausalLM
tokenizer_type: PreTrainedTokenizerFast
#overrides_of_model_config:
# rope_scaling:
# type: linear
# factor: 4
special_tokens:
pad_token: "<|end_of_text|>"
gptq: false
gptq_disable_exllama: true
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: /workspace/axolotl/output.jsonl
ds_type: json
type: completion
data_files:
- /workspace/axolotl/output.jsonl
output_dir: ./2-qlora-out-l3-10
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 90
lora_dropout: 0.10
lora_target_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
peft_use_dora: true
wandb_project: kalomaze-model
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 4
# optimizer: paged_adamw_8bit
# optimizer: adamw_bnb_8bit
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000015
cosine_min_lr_ratio: 0.2
max_grad_norm: 1.0
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 0
saves_per_epoch: 2
save_total_limit: 7
debug:
weight_decay: 0.0
# fsdp:
# - full_shard
# - auto_wrap
# fsdp_config:
# fsdp_limit_all_gathers: true
# fsdp_sync_module_states: true
# fsdp_offload_params: false
# fsdp_use_orig_params: false
# fsdp_cpu_ram_efficient_loading: false
# fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
# fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
# fsdp_state_dict_type: FULL_STATE_DICT
seed: 246
```
</details><br>
# 2-qlora-out-l3-10
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-70B](https://huggingface.co/NousResearch/Meta-Llama-3-70B) 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: 1.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 246
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NousResearch/Meta-Llama-3-70B", "model-index": [{"name": "2-qlora-out-l3-10", "results": []}]} | wave-on-discord/llama-3-70b-llc-3 | null | [
"peft",
"llama",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-70B",
"license:other",
"4-bit",
"region:us"
]
| null | 2024-04-25T18:17:39+00:00 |
null | null |
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) with an unquantized embedding layer.
| {"license": "mit"} | numen-tech/Phi-3-mini-4k-instruct-w4a16g128asym_1 | null | [
"arxiv:2308.13137",
"license:mit",
"region:us"
]
| null | 2024-04-25T18:20:22+00:00 |
sentence-similarity | sentence-transformers |
# nomic-embed-text-v1.5: Resizable Production Embeddings with Matryoshka Representation Learning
`nomic-embed-text-v1.5` is an improvement upon [Nomic Embed](https://huggingface.co/nomic-ai/nomic-embed-text-v1) that utilizes [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147) which gives developers the flexibility to trade off the embedding size for a negligible reduction in performance.
| Name | SeqLen | Dimension | MTEB |
| :-------------------------------:| :----- | :-------- | :------: |
| nomic-embed-text-v1 | 8192 | 768 | **62.39** |
| nomic-embed-text-v1.5 | 8192 | 768 | 62.28 |
| nomic-embed-text-v1.5 | 8192 | 512 | 61.96 |
| nomic-embed-text-v1.5 | 8192 | 256 | 61.04 |
| nomic-embed-text-v1.5 | 8192 | 128 | 59.34 |
| nomic-embed-text-v1.5 | 8192 | 64 | 56.10 |
## Hosted Inference API
The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
Generating embeddings with the `nomic` Python client is as easy as
```python
from nomic import embed
output = embed.text(
texts=['Nomic Embedding API', '#keepAIOpen'],
model='nomic-embed-text-v1.5',
task_type='search_document',
dimensionality=256,
)
print(output)
```
For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text)
## Data Visualization
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
[](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample)
## Training Details
We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048),
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-matryoshka).
Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors)
## Usage
Note `nomic-embed-text` requires prefixes! We support the prefixes `[search_query, search_document, classification, clustering]`.
For retrieval applications, you should prepend `search_document` for all your documents and `search_query` for your queries.
### Sentence Transformers
```python
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
matryoshka_dim = 512
model = SentenceTransformer("nomic-ai/nomic-embed-text-v1.5", trust_remote_code=True)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences, convert_to_tensor=True)
embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```
### Transformers
```diff
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1.5', trust_remote_code=True, safe_serialization=True)
model.eval()
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+ matryoshka_dim = 512
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+ embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
+ embeddings = embeddings[:, :matryoshka_dim]
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)
```
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
```diff
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
```
### Transformers.js
```js
import { pipeline, layer_norm } from '@xenova/transformers';
// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1.5', {
quantized: false, // Comment out this line to use the quantized version
});
// Define sentences
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
// Compute sentence embeddings
let embeddings = await extractor(texts, { pooling: 'mean' });
console.log(embeddings); // Tensor of shape [2, 768]
const matryoshka_dim = 512;
embeddings = layer_norm(embeddings, [embeddings.dims[1]])
.slice(null, [0, matryoshka_dim])
.normalize(2, -1);
console.log(embeddings.tolist());
```
# Join the Nomic Community
- Nomic: [https://nomic.ai](https://nomic.ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
# Citation
If you find the model, dataset, or training code useful, please cite our work
```bibtex
@misc{nussbaum2024nomic,
title={Nomic Embed: Training a Reproducible Long Context Text Embedder},
author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
year={2024},
eprint={2402.01613},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | {"license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["feature-extraction", "sentence-similarity", "mteb", "transformers", "transformers.js"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "epoch_0_model", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 75.20895522388058}, {"type": "ap", "value": 38.57605549557802}, {"type": "f1", "value": 69.35586565857854}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 91.8144}, {"type": "ap", "value": 88.65222882032363}, {"type": "f1", "value": 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"mrr_at_5", "value": 36.469}, {"type": "ndcg_at_1", "value": 24.253}, {"type": "ndcg_at_10", "value": 48.010999999999996}, {"type": "ndcg_at_100", "value": 52.756}, {"type": "ndcg_at_1000", "value": 52.964999999999996}, {"type": "ndcg_at_3", "value": 36.564}, {"type": "ndcg_at_5", "value": 41.711999999999996}, {"type": "precision_at_1", "value": 24.253}, {"type": "precision_at_10", "value": 7.738}, {"type": "precision_at_100", "value": 0.98}, {"type": "precision_at_1000", "value": 0.1}, {"type": "precision_at_3", "value": 15.149000000000001}, {"type": "precision_at_5", "value": 11.593}, {"type": "recall_at_1", "value": 24.253}, {"type": "recall_at_10", "value": 77.383}, {"type": "recall_at_100", "value": 98.009}, {"type": "recall_at_1000", "value": 99.644}, {"type": "recall_at_3", "value": 45.448}, {"type": "recall_at_5", "value": 57.965999999999994}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 45.69069567851087}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringS2S", "type": "mteb/arxiv-clustering-s2s", "config": "default", "split": "test", "revision": "f910caf1a6075f7329cdf8c1a6135696f37dbd53"}, "metrics": [{"type": "v_measure", "value": 36.35185490976283}]}, {"task": {"type": "Reranking"}, "dataset": {"name": "MTEB AskUbuntuDupQuestions", "type": "mteb/askubuntudupquestions-reranking", "config": "default", "split": "test", "revision": "2000358ca161889fa9c082cb41daa8dcfb161a54"}, "metrics": [{"type": "map", "value": 61.71274951450321}, {"type": "mrr", "value": 76.06032625423207}]}, {"task": {"type": "STS"}, "dataset": {"name": "MTEB BIOSSES", "type": "mteb/biosses-sts", "config": "default", "split": "test", "revision": "d3fb88f8f02e40887cd149695127462bbcf29b4a"}, "metrics": [{"type": "cos_sim_pearson", "value": 86.73980520022269}, {"type": "cos_sim_spearman", "value": 84.24649792685918}, {"type": "euclidean_pearson", "value": 85.85197641158186}, {"type": "euclidean_spearman", "value": 84.24649792685918}, {"type": "manhattan_pearson", "value": 86.26809552711346}, {"type": "manhattan_spearman", "value": 84.56397504030865}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB Banking77Classification", "type": "mteb/banking77", "config": "default", "split": "test", "revision": "0fd18e25b25c072e09e0d92ab615fda904d66300"}, "metrics": [{"type": "accuracy", "value": 84.25324675324674}, {"type": "f1", "value": 84.17872280892557}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB BiorxivClusteringP2P", "type": "mteb/biorxiv-clustering-p2p", "config": "default", "split": "test", "revision": "65b79d1d13f80053f67aca9498d9402c2d9f1f40"}, "metrics": [{"type": "v_measure", "value": 38.770253446400886}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB BiorxivClusteringS2S", "type": "mteb/biorxiv-clustering-s2s", "config": "default", "split": "test", "revision": "258694dd0231531bc1fd9de6ceb52a0853c6d908"}, "metrics": [{"type": "v_measure", "value": 32.94307095497281}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB CQADupstackAndroidRetrieval", "type": "BeIR/cqadupstack", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 32.164}, {"type": "map_at_10", "value": 42.641}, {"type": "map_at_100", "value": 43.947}, {"type": "map_at_1000", "value": 44.074999999999996}, {"type": "map_at_3", "value": 39.592}, {"type": "map_at_5", "value": 41.204}, {"type": "mrr_at_1", "value": 39.628}, {"type": "mrr_at_10", "value": 48.625}, {"type": "mrr_at_100", "value": 49.368}, {"type": "mrr_at_1000", "value": 49.413000000000004}, {"type": "mrr_at_3", "value": 46.400000000000006}, {"type": "mrr_at_5", "value": 47.68}, {"type": "ndcg_at_1", "value": 39.628}, {"type": "ndcg_at_10", "value": 48.564}, {"type": 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{"type": "mrr_at_1", "value": 39.489999999999995}, {"type": "mrr_at_10", "value": 47.829}, {"type": "mrr_at_100", "value": 48.512}, {"type": "mrr_at_1000", "value": 48.552}, {"type": "mrr_at_3", "value": 45.754}, {"type": "mrr_at_5", "value": 46.986}, {"type": "ndcg_at_1", "value": 39.489999999999995}, {"type": "ndcg_at_10", "value": 47.269}, {"type": "ndcg_at_100", "value": 51.564}, {"type": "ndcg_at_1000", "value": 53.53099999999999}, {"type": "ndcg_at_3", "value": 43.301}, {"type": "ndcg_at_5", "value": 45.239000000000004}, {"type": "precision_at_1", "value": 39.489999999999995}, {"type": "precision_at_10", "value": 8.93}, {"type": "precision_at_100", "value": 1.415}, {"type": "precision_at_1000", "value": 0.188}, {"type": "precision_at_3", "value": 20.892}, {"type": "precision_at_5", "value": 14.865999999999998}, {"type": "recall_at_1", "value": 31.526}, {"type": "recall_at_10", "value": 56.76}, {"type": "recall_at_100", "value": 75.029}, {"type": "recall_at_1000", "value": 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{"type": "map_at_3", "value": 21.912000000000003}, {"type": "map_at_5", "value": 23.257}, {"type": "mrr_at_1", "value": 20.274}, {"type": "mrr_at_10", "value": 28.552}, {"type": "mrr_at_100", "value": 29.42}, {"type": "mrr_at_1000", "value": 29.497}, {"type": "mrr_at_3", "value": 26.14}, {"type": "mrr_at_5", "value": 27.502}, {"type": "ndcg_at_1", "value": 20.274}, {"type": "ndcg_at_10", "value": 29.088}, {"type": "ndcg_at_100", "value": 34.293}, {"type": "ndcg_at_1000", "value": 37.271}, {"type": "ndcg_at_3", "value": 24.708}, {"type": "ndcg_at_5", "value": 26.809}, {"type": "precision_at_1", "value": 20.274}, {"type": "precision_at_10", "value": 5.361}, {"type": "precision_at_100", "value": 0.915}, {"type": "precision_at_1000", "value": 0.13}, {"type": "precision_at_3", "value": 11.733}, {"type": "precision_at_5", "value": 8.556999999999999}, {"type": "recall_at_1", "value": 16.715}, {"type": "recall_at_10", "value": 39.587}, {"type": "recall_at_100", "value": 62.336000000000006}, {"type": "recall_at_1000", "value": 83.453}, {"type": "recall_at_3", "value": 27.839999999999996}, {"type": "recall_at_5", "value": 32.952999999999996}, {"type": "map_at_1", "value": 28.793000000000003}, {"type": "map_at_10", "value": 38.582}, {"type": "map_at_100", "value": 39.881}, {"type": "map_at_1000", "value": 39.987}, {"type": "map_at_3", "value": 35.851}, {"type": "map_at_5", "value": 37.289}, {"type": "mrr_at_1", "value": 34.455999999999996}, {"type": "mrr_at_10", "value": 43.909}, {"type": "mrr_at_100", "value": 44.74}, {"type": "mrr_at_1000", "value": 44.786}, {"type": "mrr_at_3", "value": 41.659}, {"type": "mrr_at_5", "value": 43.010999999999996}, {"type": "ndcg_at_1", "value": 34.455999999999996}, {"type": "ndcg_at_10", "value": 44.266}, {"type": "ndcg_at_100", "value": 49.639}, {"type": "ndcg_at_1000", "value": 51.644}, {"type": "ndcg_at_3", "value": 39.865}, {"type": "ndcg_at_5", "value": 41.887}, {"type": "precision_at_1", "value": 34.455999999999996}, {"type": "precision_at_10", "value": 7.843999999999999}, {"type": "precision_at_100", "value": 1.243}, {"type": "precision_at_1000", "value": 0.158}, {"type": "precision_at_3", "value": 18.831999999999997}, {"type": "precision_at_5", "value": 13.147}, {"type": "recall_at_1", "value": 28.793000000000003}, {"type": "recall_at_10", "value": 55.68300000000001}, {"type": "recall_at_100", "value": 77.99000000000001}, {"type": "recall_at_1000", "value": 91.183}, {"type": "recall_at_3", "value": 43.293}, {"type": "recall_at_5", "value": 48.618}, {"type": "map_at_1", "value": 25.907000000000004}, {"type": "map_at_10", "value": 35.519}, {"type": "map_at_100", "value": 36.806}, {"type": "map_at_1000", "value": 36.912}, {"type": "map_at_3", "value": 32.748}, {"type": "map_at_5", "value": 34.232}, {"type": "mrr_at_1", "value": 31.621}, {"type": "mrr_at_10", "value": 40.687}, {"type": "mrr_at_100", "value": 41.583}, {"type": "mrr_at_1000", "value": 41.638999999999996}, {"type": "mrr_at_3", "value": 38.527}, 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"ndcg_at_3", "value": 88.877}, {"type": "ndcg_at_5", "value": 86.05199999999999}, {"type": "precision_at_1", "value": 94}, {"type": "precision_at_10", "value": 87}, {"type": "precision_at_100", "value": 63.38}, {"type": "precision_at_1000", "value": 23.498}, {"type": "precision_at_3", "value": 94}, {"type": "precision_at_5", "value": 92}, {"type": "recall_at_1", "value": 0.242}, {"type": "recall_at_10", "value": 2.302}, {"type": "recall_at_100", "value": 14.979000000000001}, {"type": "recall_at_1000", "value": 49.638}, {"type": "recall_at_3", "value": 0.753}, {"type": "recall_at_5", "value": 1.226}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB Touche2020", "type": "webis-touche2020", "config": "default", "split": "test", "revision": "None"}, "metrics": [{"type": "map_at_1", "value": 3.006}, {"type": "map_at_10", "value": 11.805}, {"type": "map_at_100", "value": 18.146}, {"type": "map_at_1000", "value": 19.788}, {"type": "map_at_3", "value": 5.914}, {"type": "map_at_5", "value": 8.801}, {"type": "mrr_at_1", "value": 40.816}, {"type": "mrr_at_10", "value": 56.36600000000001}, {"type": "mrr_at_100", "value": 56.721999999999994}, {"type": "mrr_at_1000", "value": 56.721999999999994}, {"type": "mrr_at_3", "value": 52.041000000000004}, {"type": "mrr_at_5", "value": 54.796}, {"type": "ndcg_at_1", "value": 37.755}, {"type": "ndcg_at_10", "value": 29.863}, {"type": "ndcg_at_100", "value": 39.571}, {"type": "ndcg_at_1000", "value": 51.385999999999996}, {"type": "ndcg_at_3", "value": 32.578}, {"type": "ndcg_at_5", "value": 32.351}, {"type": "precision_at_1", "value": 40.816}, {"type": "precision_at_10", "value": 26.531}, {"type": "precision_at_100", "value": 7.796}, {"type": "precision_at_1000", "value": 1.555}, {"type": "precision_at_3", "value": 32.653}, {"type": "precision_at_5", "value": 33.061}, {"type": "recall_at_1", "value": 3.006}, {"type": "recall_at_10", "value": 18.738}, {"type": "recall_at_100", "value": 48.058}, {"type": "recall_at_1000", "value": 83.41300000000001}, {"type": "recall_at_3", "value": 7.166}, {"type": "recall_at_5", "value": 12.102}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB ToxicConversationsClassification", "type": "mteb/toxic_conversations_50k", "config": "default", "split": "test", "revision": "d7c0de2777da35d6aae2200a62c6e0e5af397c4c"}, "metrics": [{"type": "accuracy", "value": 71.4178}, {"type": "ap", "value": 14.648781342150446}, {"type": "f1", "value": 55.07299194946378}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB TweetSentimentExtractionClassification", "type": "mteb/tweet_sentiment_extraction", "config": "default", "split": "test", "revision": "d604517c81ca91fe16a244d1248fc021f9ecee7a"}, "metrics": [{"type": "accuracy", "value": 60.919637804187886}, {"type": "f1", "value": 61.24122013967399}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB TwentyNewsgroupsClustering", "type": "mteb/twentynewsgroups-clustering", "config": "default", "split": "test", "revision": "6125ec4e24fa026cec8a478383ee943acfbd5449"}, "metrics": [{"type": "v_measure", "value": 49.207896583685695}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 86.23114978840078}, {"type": "cos_sim_ap", "value": 74.26624727825818}, {"type": "cos_sim_f1", "value": 68.72377190817083}, {"type": "cos_sim_precision", "value": 64.56400742115028}, {"type": "cos_sim_recall", "value": 73.45646437994723}, {"type": "dot_accuracy", "value": 86.23114978840078}, {"type": "dot_ap", "value": 74.26624032659652}, {"type": "dot_f1", "value": 68.72377190817083}, {"type": "dot_precision", "value": 64.56400742115028}, {"type": "dot_recall", "value": 73.45646437994723}, {"type": "euclidean_accuracy", "value": 86.23114978840078}, {"type": "euclidean_ap", "value": 74.26624714480556}, {"type": "euclidean_f1", "value": 68.72377190817083}, {"type": "euclidean_precision", "value": 64.56400742115028}, {"type": "euclidean_recall", "value": 73.45646437994723}, {"type": "manhattan_accuracy", "value": 86.16558383501221}, {"type": "manhattan_ap", "value": 74.2091943976357}, {"type": "manhattan_f1", "value": 68.64221520524654}, {"type": "manhattan_precision", "value": 63.59135913591359}, {"type": "manhattan_recall", "value": 74.5646437994723}, {"type": "max_accuracy", "value": 86.23114978840078}, {"type": "max_ap", "value": 74.26624727825818}, {"type": "max_f1", "value": 68.72377190817083}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 89.3681841114604}, {"type": "cos_sim_ap", "value": 86.65166387498546}, {"type": "cos_sim_f1", "value": 79.02581944698774}, {"type": "cos_sim_precision", "value": 75.35796605434099}, {"type": "cos_sim_recall", "value": 83.06898675700647}, {"type": "dot_accuracy", "value": 89.3681841114604}, {"type": "dot_ap", "value": 86.65166019802056}, {"type": "dot_f1", "value": 79.02581944698774}, {"type": "dot_precision", "value": 75.35796605434099}, {"type": "dot_recall", "value": 83.06898675700647}, {"type": "euclidean_accuracy", "value": 89.3681841114604}, {"type": "euclidean_ap", "value": 86.65166462876266}, {"type": "euclidean_f1", "value": 79.02581944698774}, {"type": "euclidean_precision", "value": 75.35796605434099}, {"type": "euclidean_recall", "value": 83.06898675700647}, {"type": "manhattan_accuracy", "value": 89.36624364497226}, {"type": "manhattan_ap", "value": 86.65076471274106}, {"type": "manhattan_f1", "value": 79.07408783532733}, {"type": "manhattan_precision", "value": 76.41102972856527}, {"type": "manhattan_recall", "value": 81.92947336002464}, {"type": "max_accuracy", "value": 89.3681841114604}, {"type": "max_ap", "value": 86.65166462876266}, {"type": "max_f1", "value": 79.07408783532733}]}]}]} | lightbird-ai/nomic | null | [
"sentence-transformers",
"onnx",
"safetensors",
"nomic_bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"transformers",
"transformers.js",
"custom_code",
"arxiv:2205.13147",
"arxiv:2402.01613",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:20:33+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codet5-fine-tuned
This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1944
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.98 | 42 | 0.2024 |
| No log | 1.96 | 84 | 0.1944 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "bsd-3-clause", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5p-220m", "model-index": [{"name": "codet5-fine-tuned", "results": []}]} | cincin2399/codet5-fine-tuned | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5p-220m",
"license:bsd-3-clause",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:21:01+00:00 |
text-generation | transformers | # AgentPublic/guillaumetell-7b AWQ
- Model creator: [AgentPublic](https://huggingface.co/AgentPublic)
- Original model: [guillaumetell-7b](https://huggingface.co/AgentPublic/guillaumetell-7b)
## Model Summary
**Guillaume Tell** est un Large Language Model (LLM) français basé sur Mistral Open-Hermes 2.5 optimisé pour le RAG (Retrieval Augmented Generation) avec traçabilité des sources et explicabilité.
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/guillaumetell-7b-AWQ"
system_message = "You are guillaumetell-7b, incarnated as a powerful AI. You were created by AgentPublic."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### 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.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [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
| {"language": ["fr"], "license": "apache-2.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/guillaumetell-7b-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"fr",
"license:apache-2.0",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:24:19+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** ppopiolek
- **License:** apache-2.0
- **Finetuned from model :** TinyLlama/TinyLlama-1.1B-Chat-v1.0
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | ppopiolek/tinyllama_eng_short | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:24:29+00:00 |
text-to-image | diffusers |
# LoRA text2image fine-tuning - animanatwork/illustrations-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the animanatwork/text_to_image_dataset dataset.
Below, we can find some images from the dataset:
<div style="display: flex; justify-content: space-between;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/fHCi3t9AlK5AasMt_K0nh.png" width="30%" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/fYdTOG8QKtUHKvDOBw40r.png" width="30%" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/66297c313291276a14318d23/IXx2U6cM0SH4CFGw1qmjE.png" width="30%" />
</div>
The images below are generated from the model using the prompt: "a stylized illustration of a woman sitting in a comfortable chair, reading a book. She is wearing a hat, and her expression appears focused and calm. A black cat is also depicted, sitting beside her and looking at the book, suggesting a shared moment of quiet and companionship. The woman is dressed in a casual outfit with yellow shoes, and the overall color scheme is simple, using black, white, and yellow. The setting seems cozy and peaceful, ideal for reading."
<div style="display: flex; justify-content: space-between;">
<img src="./image_0.png" width="25%" />
<img src="./image_1.png" width="25%" />
<img src="./image_2.png" width="25%" />
<img src="./image_3.png" width="25%" />
</div>
## Intended uses & limitations
Do NOT use in production. This model was purely created for research purposes.
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
- The model was trained on the "animanatwork/text_to_image_dataset" dataset using 10_000 training step (default is 15_000) and took several hours to train. For more details see [Colab notebook](https://colab.research.google.com/drive/1CePJWR2sfYW-w0oPuiIdJzuc82Z6yYHt#scrollTo=QzKEQJYkUv2Q).
- The dataset's tokens were generated using chatGPT vision. During training, I noticed CLIP can only use 77 tokens for a given image. Since most of our image descriptions contained more tokens, we'll have to create a new dataset that doesn't exceed the maximum.
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "diffusers-training", "lora"], "base_model": "runwayml/stable-diffusion-v1-5", "inference": true} | animanatwork/illustrations-lora | null | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-04-25T18:25:25+00:00 |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nafizshahriar -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nafizshahriar -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nafizshahriar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "761.00 +/- 230.51", "name": "mean_reward", "verified": false}]}]}]} | nafizshahriar/NF-dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-25T18:25:45+00:00 |
text-generation | transformers | {} | mzman123/musa-chef-gpt | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:27:21+00:00 |
|
question-answering | transformers | {} | lanzv/ClinicalBERTPRQABCZ_70_111_CS | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:27:49+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-2
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-2", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:29:44+00:00 |
question-answering | transformers | {} | SachinSharma0325/span_nz | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:32:35+00:00 |
|
text-classification | transformers | {} | Fabchi/ENVIRONMENTALBERT | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:33:15+00:00 |
|
text-generation | transformers | {} | vicaloy/llama-2-7b-raft-software-life-cycle-models-esp | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:33:21+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M-lora-binding-sites_2024-04-25_14-35-31
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3589
- Accuracy: 0.8457
## 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: 0.0005701568055793089
- train_batch_size: 64
- eval_batch_size: 64
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6703 | 1.0 | 24 | 0.6807 | 0.5820 |
| 0.6449 | 2.0 | 48 | 0.6703 | 0.5820 |
| 0.6659 | 3.0 | 72 | 0.6458 | 0.5977 |
| 0.6432 | 4.0 | 96 | 0.6612 | 0.6328 |
| 0.6322 | 5.0 | 120 | 0.6051 | 0.6523 |
| 0.6176 | 6.0 | 144 | 0.6062 | 0.6504 |
| 0.4904 | 7.0 | 168 | 0.5762 | 0.6777 |
| 0.4426 | 8.0 | 192 | 0.5784 | 0.6953 |
| 0.6014 | 9.0 | 216 | 0.5497 | 0.7148 |
| 0.4484 | 10.0 | 240 | 0.5399 | 0.7227 |
| 0.552 | 11.0 | 264 | 0.5142 | 0.7480 |
| 0.3581 | 12.0 | 288 | 0.4395 | 0.7930 |
| 0.3604 | 13.0 | 312 | 0.4201 | 0.8066 |
| 0.2733 | 14.0 | 336 | 0.4107 | 0.8262 |
| 0.2539 | 15.0 | 360 | 0.4373 | 0.8008 |
| 0.3538 | 16.0 | 384 | 0.3954 | 0.8301 |
| 0.4363 | 17.0 | 408 | 0.3852 | 0.8320 |
| 0.3433 | 18.0 | 432 | 0.3735 | 0.8418 |
| 0.2758 | 19.0 | 456 | 0.3685 | 0.8438 |
| 0.2073 | 20.0 | 480 | 0.3860 | 0.8262 |
| 0.3578 | 21.0 | 504 | 0.3689 | 0.8301 |
| 0.3114 | 22.0 | 528 | 0.3626 | 0.8418 |
| 0.3296 | 23.0 | 552 | 0.3621 | 0.8438 |
| 0.276 | 24.0 | 576 | 0.3602 | 0.8457 |
| 0.2583 | 25.0 | 600 | 0.3622 | 0.8457 |
| 0.1917 | 26.0 | 624 | 0.3597 | 0.8477 |
| 0.3588 | 27.0 | 648 | 0.3603 | 0.8477 |
| 0.219 | 28.0 | 672 | 0.3606 | 0.8438 |
| 0.3091 | 29.0 | 696 | 0.3586 | 0.8457 |
| 0.2235 | 30.0 | 720 | 0.3589 | 0.8457 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/esm2_t12_35M_UR50D", "model-index": [{"name": "esm2_t12_35M-lora-binding-sites_2024-04-25_14-35-31", "results": []}]} | wcvz/esm2_t12_35M-lora-binding-sites_2024-04-25_14-35-31 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-04-25T18:35:31+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | ciaranmacseoin/research_paper_extractor | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:35:56+00:00 |
null | null | ## How to Get Started with the Model
To use this model, you can either interact with it programmatically using the Python code below or through a web-based interface provided by Gradio.
### Using Python Code
```python
from transformers import TFAutoModelForImageClassification, AutoTokenizer
import gradio as gr
# Laden Sie das Modell und den Tokenizer von Hugging Face herunter
model = TFAutoModelForImageClassification.from_pretrained("kiki7555/pokemon_classifier_tf")
tokenizer = AutoTokenizer.from_pretrained("kiki7555/pokemon_classifier_tf")
def predict_pokemon(image):
# Hier kannst du die Bildvorverarbeitung und -nachverarbeitung hinzufügen
# ...
# Vorhersage treffen
predictions = model.predict(image) # Hier musst du die genaue Vorverarbeitung für das Bild hinzufügen
predicted_class = predictions.argmax()
class_names = ['Charizard', 'Pikachu', 'Zapdos']
return class_names[predicted_class]
# Gradio UI erstellen
image_input = gr.inputs.Image(shape=(128, 128))
output_text = gr.outputs.Textbox()
gr.Interface(
fn=predict_pokemon,
inputs=image_input,
outputs=output_text,
title="Pokemon Classifier",
description="Classify images of Pokemon into three categories: Charizard, Pikachu, and Zapdos."
).launch()
| {} | kiki7555/pokemon_classifier_tf.keras | null | [
"region:us"
]
| null | 2024-04-25T18:37:17+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_seq_clf_model
This model is a fine-tuned version of [kssteven/ibert-roberta-base](https://huggingface.co/kssteven/ibert-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1998
- Accuracy: 0.9526
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1969 | 1.0 | 1563 | 0.1395 | 0.9493 |
| 0.1245 | 2.0 | 3126 | 0.1998 | 0.9526 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "kssteven/ibert-roberta-base", "model-index": [{"name": "my_awesome_seq_clf_model", "results": []}]} | tristayqc/my_awesome_seq_clf_model | null | [
"transformers",
"tensorboard",
"safetensors",
"ibert",
"text-classification",
"generated_from_trainer",
"base_model:kssteven/ibert-roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:39:14+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/SparseLLM/ReluLLaMA-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ReluLLaMA-70B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ReluLLaMA-70B-GGUF/resolve/main/ReluLLaMA-70B.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "llama2", "library_name": "transformers", "base_model": "SparseLLM/ReluLLaMA-70B", "quantized_by": "mradermacher"} | mradermacher/ReluLLaMA-70B-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:SparseLLM/ReluLLaMA-70B",
"license:llama2",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:39:15+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gemma-2b - bnb 4bits
- Model creator: https://huggingface.co/alpindale/
- Original model: https://huggingface.co/alpindale/gemma-2b/
Original model description:
---
library_name: transformers
tags: []
extra_gated_heading: "Access Gemma on Hugging Face"
extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
extra_gated_button_content: "Acknowledge license"
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning the model
You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`.
In that repository, we provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(**input_text, return_tensors="pt")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **54.0** | **56.4** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
| {} | RichardErkhov/alpindale_-_gemma-2b-4bits | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-25T18:40:06+00:00 |
text2text-generation | transformers | {} | neal61/bikes-ops-t5-small-23 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:40:12+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
pygmalion-instruct - bnb 4bits
- Model creator: https://huggingface.co/alpindale/
- Original model: https://huggingface.co/alpindale/pygmalion-instruct/
Original model description:
---
license: mit
---
## Model Details
Experimental model. Trained with the [Pygmalion](https://huggingface.co/PygmalionAI/pygmalion-6b/tree/dev) and the [WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored) datasets.
The purpose of this model is to enable complex Instruct prompting but with the RP capabilties of Pygmalion.
### Prompting format
```
instruction:
output:
```
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
### Uses
The intended use-case is Role-Playing with Instruct prompts. Guiding the bot towards a certain conversation style should be easier this way. Subject to experimentation.
### Out-of-Scope Use
- Assistant Bot [subject to providing incorrect instructions]
- Complex multi-character chat
### Risks
The model can generate potentially harmful or NSFW outputs. Please use with caution.
### Citation
WizardLM:
```
@misc{xu2023wizardlm,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| {} | RichardErkhov/alpindale_-_pygmalion-instruct-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2304.12244",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-25T18:43:36+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | NiCoSav/lora_model | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:45:11+00:00 |
null | null | {} | ashishp-wiai/Rice_LoRA_90-2024-04-25 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-25T18:45:13+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** jurieyel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | jurieyel/text2sql-finetuned-llama3-8b-bnb-4bit_6k | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:45:31+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-1", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-130_PasswordMatch_n-its-10-seed-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:45:54+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small urdu 3 - huzaifa
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["ur"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small urdu 3 - huzaifa", "results": []}]} | huzaifa1117/whisper-small-urdu-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:46:08+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M-lora-binding-sites_2024-04-25_14-47-08
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4214
- Accuracy: 0.8574
## 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: 0.0005701568055793089
- train_batch_size: 64
- eval_batch_size: 64
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6683 | 1.0 | 24 | 0.6799 | 0.5820 |
| 0.6546 | 2.0 | 48 | 0.6737 | 0.5820 |
| 0.665 | 3.0 | 72 | 0.6597 | 0.5820 |
| 0.6569 | 4.0 | 96 | 0.6247 | 0.6426 |
| 0.6524 | 5.0 | 120 | 0.6101 | 0.6582 |
| 0.6161 | 6.0 | 144 | 0.5936 | 0.6699 |
| 0.4919 | 7.0 | 168 | 0.5802 | 0.6680 |
| 0.461 | 8.0 | 192 | 0.6265 | 0.6465 |
| 0.6359 | 9.0 | 216 | 0.5477 | 0.7051 |
| 0.4399 | 10.0 | 240 | 0.5543 | 0.7109 |
| 0.7217 | 11.0 | 264 | 0.6668 | 0.6719 |
| 0.4323 | 12.0 | 288 | 0.4740 | 0.7656 |
| 0.4103 | 13.0 | 312 | 0.4999 | 0.7637 |
| 0.2916 | 14.0 | 336 | 0.3996 | 0.8320 |
| 0.262 | 15.0 | 360 | 0.4088 | 0.8418 |
| 0.4494 | 16.0 | 384 | 0.4432 | 0.8164 |
| 0.3895 | 17.0 | 408 | 0.3702 | 0.8379 |
| 0.3254 | 18.0 | 432 | 0.3501 | 0.8438 |
| 0.2065 | 19.0 | 456 | 0.3646 | 0.8438 |
| 0.167 | 20.0 | 480 | 0.3768 | 0.8320 |
| 0.3051 | 21.0 | 504 | 0.3557 | 0.8457 |
| 0.2773 | 22.0 | 528 | 0.3551 | 0.8730 |
| 0.2969 | 23.0 | 552 | 0.3434 | 0.8555 |
| 0.1427 | 24.0 | 576 | 0.3390 | 0.8594 |
| 0.327 | 25.0 | 600 | 0.4370 | 0.8652 |
| 0.1195 | 26.0 | 624 | 0.3594 | 0.8496 |
| 0.3383 | 27.0 | 648 | 0.4215 | 0.8672 |
| 0.1738 | 28.0 | 672 | 0.3671 | 0.8711 |
| 0.2686 | 29.0 | 696 | 0.3913 | 0.8457 |
| 0.1049 | 30.0 | 720 | 0.3803 | 0.8652 |
| 0.1809 | 31.0 | 744 | 0.4294 | 0.8691 |
| 0.1036 | 32.0 | 768 | 0.4279 | 0.8613 |
| 0.1664 | 33.0 | 792 | 0.4326 | 0.8594 |
| 0.246 | 34.0 | 816 | 0.4770 | 0.8535 |
| 0.0664 | 35.0 | 840 | 0.5014 | 0.8516 |
| 0.1116 | 36.0 | 864 | 0.5981 | 0.8555 |
| 0.0323 | 37.0 | 888 | 0.5228 | 0.8633 |
| 0.0751 | 38.0 | 912 | 0.5393 | 0.8594 |
| 0.0659 | 39.0 | 936 | 0.5420 | 0.8555 |
| 0.0699 | 40.0 | 960 | 0.5920 | 0.8535 |
| 0.0427 | 41.0 | 984 | 0.6336 | 0.8555 |
| 0.0265 | 42.0 | 1008 | 0.6485 | 0.8594 |
| 0.0386 | 43.0 | 1032 | 0.6955 | 0.8516 |
| 0.0759 | 44.0 | 1056 | 0.8761 | 0.8555 |
| 0.164 | 45.0 | 1080 | 0.8223 | 0.8496 |
| 0.0632 | 46.0 | 1104 | 0.8234 | 0.8594 |
| 0.0709 | 47.0 | 1128 | 0.8806 | 0.8535 |
| 0.0042 | 48.0 | 1152 | 0.9198 | 0.8594 |
| 0.0198 | 49.0 | 1176 | 0.8870 | 0.8652 |
| 0.002 | 50.0 | 1200 | 0.9676 | 0.8496 |
| 0.0156 | 51.0 | 1224 | 0.9507 | 0.8613 |
| 0.0551 | 52.0 | 1248 | 0.9955 | 0.8555 |
| 0.018 | 53.0 | 1272 | 1.0277 | 0.8535 |
| 0.0041 | 54.0 | 1296 | 1.0293 | 0.8633 |
| 0.0021 | 55.0 | 1320 | 1.0939 | 0.8652 |
| 0.0851 | 56.0 | 1344 | 1.1512 | 0.8574 |
| 0.0257 | 57.0 | 1368 | 1.0998 | 0.8516 |
| 0.0364 | 58.0 | 1392 | 1.1812 | 0.8496 |
| 0.0019 | 59.0 | 1416 | 1.1941 | 0.8438 |
| 0.0015 | 60.0 | 1440 | 1.2219 | 0.8574 |
| 0.0868 | 61.0 | 1464 | 1.2075 | 0.8555 |
| 0.0002 | 62.0 | 1488 | 1.2761 | 0.8574 |
| 0.0005 | 63.0 | 1512 | 1.2235 | 0.8535 |
| 0.0149 | 64.0 | 1536 | 1.2502 | 0.8613 |
| 0.002 | 65.0 | 1560 | 1.2890 | 0.8477 |
| 0.0001 | 66.0 | 1584 | 1.2766 | 0.8496 |
| 0.0488 | 67.0 | 1608 | 1.2966 | 0.8496 |
| 0.0002 | 68.0 | 1632 | 1.3242 | 0.8535 |
| 0.0008 | 69.0 | 1656 | 1.3247 | 0.8535 |
| 0.0024 | 70.0 | 1680 | 1.3615 | 0.8613 |
| 0.0001 | 71.0 | 1704 | 1.3805 | 0.8574 |
| 0.0017 | 72.0 | 1728 | 1.3145 | 0.8555 |
| 0.0004 | 73.0 | 1752 | 1.3214 | 0.8613 |
| 0.0121 | 74.0 | 1776 | 1.3500 | 0.8613 |
| 0.0229 | 75.0 | 1800 | 1.3902 | 0.8516 |
| 0.0022 | 76.0 | 1824 | 1.3923 | 0.8555 |
| 0.0007 | 77.0 | 1848 | 1.3887 | 0.8496 |
| 0.0036 | 78.0 | 1872 | 1.3787 | 0.8535 |
| 0.0001 | 79.0 | 1896 | 1.3920 | 0.8535 |
| 0.0 | 80.0 | 1920 | 1.3965 | 0.8574 |
| 0.0008 | 81.0 | 1944 | 1.3935 | 0.8633 |
| 0.0 | 82.0 | 1968 | 1.3969 | 0.8594 |
| 0.0 | 83.0 | 1992 | 1.3986 | 0.8574 |
| 0.0001 | 84.0 | 2016 | 1.3891 | 0.8594 |
| 0.0017 | 85.0 | 2040 | 1.4158 | 0.8633 |
| 0.0002 | 86.0 | 2064 | 1.4081 | 0.8574 |
| 0.0054 | 87.0 | 2088 | 1.4131 | 0.8613 |
| 0.0002 | 88.0 | 2112 | 1.4065 | 0.8633 |
| 0.0108 | 89.0 | 2136 | 1.4221 | 0.8613 |
| 0.0002 | 90.0 | 2160 | 1.4166 | 0.8613 |
| 0.0 | 91.0 | 2184 | 1.4192 | 0.8555 |
| 0.0 | 92.0 | 2208 | 1.4152 | 0.8613 |
| 0.0001 | 93.0 | 2232 | 1.4160 | 0.8613 |
| 0.0412 | 94.0 | 2256 | 1.4141 | 0.8613 |
| 0.0001 | 95.0 | 2280 | 1.4159 | 0.8613 |
| 0.0073 | 96.0 | 2304 | 1.4179 | 0.8613 |
| 0.0 | 97.0 | 2328 | 1.4222 | 0.8633 |
| 0.0209 | 98.0 | 2352 | 1.4202 | 0.8594 |
| 0.0001 | 99.0 | 2376 | 1.4203 | 0.8594 |
| 0.0001 | 100.0 | 2400 | 1.4214 | 0.8574 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/esm2_t12_35M_UR50D", "model-index": [{"name": "esm2_t12_35M-lora-binding-sites_2024-04-25_14-47-08", "results": []}]} | wcvz/esm2_t12_35M-lora-binding-sites_2024-04-25_14-47-08 | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t12_35M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-04-25T18:47:08+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9501
- Bleu: 0.3341
- Gen Len: 18.1659
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.342 | 1.0 | 1617 | 2.9981 | 0.3207 | 18.1549 |
| 3.2797 | 2.0 | 3234 | 2.9501 | 0.3341 | 18.1659 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_opus_books_model", "results": []}]} | kellyjiayixu/my_awesome_opus_books_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:48:21+00:00 |
text-generation | transformers |
# KangalKhan-Alpha-Rubyroid-7B-Fixed
KangalKhan-Alpha-Rubyroid-7B-Fixed is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed](https://huggingface.co/Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed)
* [argilla/distilabeled-OpenHermes-2.5-Mistral-7B](https://huggingface.co/argilla/distilabeled-OpenHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed
layer_range: [0, 32]
- model: argilla/distilabeled-OpenHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed
parameters:
t:
- filter: self_attn
value: [1, 0.5, 0.7, 0.3, 0]
- filter: mlp
value: [0, 0.5, 0.3, 0.7, 1]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Yuma42/KangalKhan-Alpha-Rubyroid-7B-Fixed"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed", "argilla/distilabeled-OpenHermes-2.5-Mistral-7B"], "base_model": ["Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed", "argilla/distilabeled-OpenHermes-2.5-Mistral-7B"]} | Yuma42/KangalKhan-Alpha-Rubyroid-7B-Fixed | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed",
"argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"conversational",
"en",
"base_model:Yuma42/KangalKhan-Alpha-Sapphiroid-7B-Fixed",
"base_model:argilla/distilabeled-OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:48:37+00:00 |
text-generation | transformers | # MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3 AWQ
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [Llama-3-8B-Instruct-DPO-v0.3](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3)
<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
## Model Summary
This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-8B-Instruct` model. I have used `rope_theta` to extend the context length up to 32K safely.
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Llama-3-8B-Instruct-DPO-v0.3-AWQ"
system_message = "You are Llama-3-8B-Instruct-DPO-v0.3, incarnated as a powerful AI. You were created by MaziyarPanahi."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### 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.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [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
| {"license": "llama3", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible", "axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3"], "datasets": ["Intel/orca_dpo_pairs"], "model_name": "Llama-3-8B-Instruct-DPO-v0.3", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "Suparious"} | solidrust/Llama-3-8B-Instruct-DPO-v0.3-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"axolotl",
"finetune",
"dpo",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"dataset:Intel/orca_dpo_pairs",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:48:39+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** sireskay
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | sireskay/llama3-8b-oig-unsloth-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:49:00+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-toxic2nontoxic-100-filtered-50-0.0006 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:49:04+00:00 |
text-generation | transformers |
<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# MaziyarPanahi/Llama-3-8B-Instruct-64k
This model has been made based on a great of [@winglian](https://huggingface.co/winglian/) with his latest model [winglian/Llama-3-8b-64k-PoSE](https://huggingface.co/winglian/Llama-3-8b-64k-PoSE/)
> This model uses [PoSE](https://huggingface.co/papers/2309.10400) to extend Llama's context length from 8k to 64k @ rope_theta: 500000.0.
> We used PoSE with continued pretraining on 300M tokens from the RedPajama V1 dataset using data between 6k-8k tokens.
> We have further set rope_theta to 2M after continued pre-training to potentially further extend the context past 64k.
> This was trained on a subset of the RedPajama v1 dataset with text between 6k-8k context. We trained a rank stabilized LoRA of rank 256. [WandB](https://wandb.ai/oaaic/llama-3-64k/runs/tkcyjt37)
# Quantized GGUF
All GGUF models come with context length of `64000`: [MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-64k-GGUF)
# How to use
You can use this model by using `MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3` as the model name in Hugging Face's
transformers library.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch
model_id = "MaziyarPanahi/Llama-3-8B-Instruct-64k"
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
# attn_implementation="flash_attention_2"
)
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True
)
streamer = TextStreamer(tokenizer)
pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
model_kwargs={"torch_dtype": torch.bfloat16},
streamer=streamer
)
# Then you can use the pipeline to generate text.
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|im_end|>")
]
outputs = pipeline(
prompt,
max_new_tokens=8192,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.95,
)
print(outputs[0]["generated_text"][len(prompt):])
```
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["axolotl", "finetune", "dpo", "facebook", "meta", "pytorch", "llama", "llama-3", "64k", "pose"], "datasets": ["Intel/orca_dpo_pairs"], "model_name": "Llama-3-8B-Instruct-64k", "base_model": "winglian/Llama-3-8b-64k-PoSE", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "MaziyarPanahi"} | MaziyarPanahi/Llama-3-8B-Instruct-64k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"axolotl",
"finetune",
"dpo",
"facebook",
"meta",
"pytorch",
"llama-3",
"64k",
"pose",
"conversational",
"en",
"dataset:Intel/orca_dpo_pairs",
"arxiv:2309.10400",
"base_model:winglian/Llama-3-8b-64k-PoSE",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:49:31+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertje-dutch-cola
This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on [Dutch CoLA](https://huggingface.co/datasets/GroNLP/dutch-cola).
It achieves the following results on the evaluation set:
- Loss: 0.7208
- Accuracy: 0.7792
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5395 | 1.0 | 622 | 0.6046 | 0.7346 |
| 0.3626 | 2.0 | 1244 | 0.5864 | 0.7808 |
| 0.261 | 3.0 | 1866 | 0.7208 | 0.7792 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2 | {"language": ["nl"], "tags": ["generated_from_trainer"], "datasets": ["GroNLP/dutch-cola"], "metrics": ["accuracy"], "base_model": "GroNLP/bert-base-dutch-cased", "widget": [{"text": "Jan wandelt zijn schoenen."}, {"text": "Oud genoeg voor de disco is Marie nog niet."}], "model-index": [{"name": "bertje-dutch-cola", "results": []}]} | bylin/bertje-dutch-cola | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"nl",
"dataset:GroNLP/dutch-cola",
"base_model:GroNLP/bert-base-dutch-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:49:45+00:00 |
question-answering | transformers | {} | lanzv/ClinicalBERTPRQABmbert_97_992_CS | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:49:47+00:00 |
|
null | null | {} | Amityukova/BERT_directions | null | [
"region:us"
]
| null | 2024-04-25T18:50:26+00:00 |
|
sentence-similarity | sentence-transformers |
# SentenceTransformer based on google-bert/bert-base-uncased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("tomaarsen/bert-base-uncased-augmentation-indomain-nlpaug-sts")
# Run inference
sentences = [
'A woman is reading.',
'A woman is writing something.',
'A man is standing in the rain.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8682 |
| **spearman_cosine** | **0.8703** |
| pearson_manhattan | 0.8385 |
| spearman_manhattan | 0.8435 |
| pearson_euclidean | 0.8391 |
| spearman_euclidean | 0.8441 |
| pearson_dot | 0.8141 |
| spearman_dot | 0.8175 |
| pearson_max | 0.8682 |
| spearman_max | 0.8703 |
#### Semantic Similarity
* Dataset: `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8419 |
| **spearman_cosine** | **0.8363** |
| pearson_manhattan | 0.8283 |
| spearman_manhattan | 0.8261 |
| pearson_euclidean | 0.828 |
| spearman_euclidean | 0.8259 |
| pearson_dot | 0.7682 |
| spearman_dot | 0.7575 |
| pearson_max | 0.8419 |
| spearman_max | 0.8363 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
* Size: 11,498 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:----------------------------------------------------------------------|:------------------|
| <code>A plane is taking off.</code> | <code>An air plane is taking off.</code> | <code>1.0</code> |
| <code>A man is playing a large flute.</code> | <code>A man is playing a flute.</code> | <code>0.76</code> |
| <code>A man is spreading shreded cheese on a pizza.</code> | <code>A man is spreading shredded cheese on an uncooked pizza.</code> | <code>0.76</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### sentence-transformers/stsb
* Dataset: [sentence-transformers/stsb](https://huggingface.co/datasets/sentence-transformers/stsb) at [d999f12](https://huggingface.co/datasets/sentence-transformers/stsb/tree/d999f12281623b0925506817d9bd85e88289218a)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------|:------------------------------------------------------|:------------------|
| <code>A man with a hard hat is dancing.</code> | <code>A man wearing a hard hat is dancing.</code> | <code>1.0</code> |
| <code>A young child is riding a horse.</code> | <code>A child is riding a horse.</code> | <code>0.95</code> |
| <code>A man is feeding a mouse to a snake.</code> | <code>The man is feeding a mouse to the snake.</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: False
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: None
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:-----------------------:| |
| 0.1391 | 100 | 0.0572 | 0.0427 | 0.8222 | |
| 0.2782 | 200 | 0.0316 | 0.0342 | 0.8450 | |
| 0.4172 | 300 | 0.0276 | 0.0324 | 0.8621 | |
| 0.5563 | 400 | 0.0246 | 0.0300 | 0.8661 | |
| 0.6954 | 500 | 0.0206 | 0.0288 | 0.8650 | |
| 0.8345 | 600 | 0.0186 | 0.0301 | 0.8696 | |
| 0.9736 | 700 | 0.0185 | 0.0286 | 0.8703 | |
| 1.0 | 719 | - | - | 0.8363 | 0.8363 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.012 kWh
- **Carbon Emitted**: 0.005 kg of CO2
- **Hours Used**: 0.058 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "loss:CosineSimilarityLoss"], "metrics": ["pearson_cosine", "spearman_cosine", "pearson_manhattan", "spearman_manhattan", "pearson_euclidean", "spearman_euclidean", "pearson_dot", "spearman_dot", "pearson_max", "spearman_max"], "base_model": "google-bert/bert-base-uncased", "widget": [{"source_sentence": "A woman is dancing.", "sentences": ["An audience watches a girl dance.", "A man is outside on a July day.", "A man is cutting up carrots."]}, {"source_sentence": "A man shoots a man.", "sentences": ["The man is aiming a gun.", "A helicopter flies over water.", "a dog trots through the grass."]}, {"source_sentence": "A man is spitting.", "sentences": ["A man is crying.", "A helicopter flies over water.", "A slow loris hanging on a cord."]}, {"source_sentence": "A boy is vacuuming.", "sentences": ["A little boy is vacuuming the floor.", "A guy is playing an instrument.", "A woman equestrian riding a horse."]}, {"source_sentence": "A woman is reading.", "sentences": ["A woman is writing something.", "A man is standing in the rain.", "A man slices an onion."]}], "pipeline_tag": "sentence-similarity", "co2_eq_emissions": {"emissions": 4.738044659547021, "energy_consumed": 0.012189401288254294, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 0.058, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "model-index": [{"name": "SentenceTransformer based on google-bert/bert-base-uncased", "results": [{"task": {"type": "semantic-similarity", "name": "Semantic Similarity"}, "dataset": {"name": "sts test", "type": "sts-test"}, "metrics": [{"type": "pearson_cosine", "value": 0.8682431647858876, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8703313606188837, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8385159885167599, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8435007318066774, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8391102057706885, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8441165556372876, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.8140605796498762, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.8174591525223206, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8682431647858876, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8703313606188837, "name": "Spearman Max"}, {"type": "pearson_cosine", "value": 0.8418519780467144, "name": "Pearson Cosine"}, {"type": "spearman_cosine", "value": 0.8363102079867478, "name": "Spearman Cosine"}, {"type": "pearson_manhattan", "value": 0.8282641539296681, "name": "Pearson Manhattan"}, {"type": "spearman_manhattan", "value": 0.8261442750405601, "name": "Spearman Manhattan"}, {"type": "pearson_euclidean", "value": 0.8279900369159026, "name": "Pearson Euclidean"}, {"type": "spearman_euclidean", "value": 0.8258841934048688, "name": "Spearman Euclidean"}, {"type": "pearson_dot", "value": 0.7681509901549408, "name": "Pearson Dot"}, {"type": "spearman_dot", "value": 0.757455580460212, "name": "Spearman Dot"}, {"type": "pearson_max", "value": 0.8418519780467144, "name": "Pearson Max"}, {"type": "spearman_max", "value": 0.8363102079867478, "name": "Spearman Max"}]}]}]} | tomaarsen/bert-base-uncased-augmentation-indomain-nlpaug-sts | null | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"loss:CosineSimilarityLoss",
"en",
"arxiv:1908.10084",
"base_model:google-bert/bert-base-uncased",
"model-index",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:51:13+00:00 |
text-generation | transformers | {} | Nandini82/Llama-2-7b-sciq-template-finetuned | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:51:50+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** sireskay
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | sireskay/llama3-8b-oig-unsloth | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:51:56+00:00 |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'tarpalsus/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-154.64 +/- 103.42", "name": "mean_reward", "verified": false}]}]}]} | tarpalsus/LunarLander-v2 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| null | 2024-04-25T18:53:08+00:00 |
null | null | {"language": ["es"]} | Camila1325/kdflsjd | null | [
"es",
"region:us"
]
| null | 2024-04-25T18:53:28+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | 21bce239/model_dl_45 | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T18:53:32+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-finetuned-en-to-es-eval1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "t5-base", "model-index": [{"name": "t5-finetuned-en-to-es-eval1", "results": []}]} | tsetsuuhei/t5-finetuned-en-to-es-eval1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T18:54:27+00:00 |
text-generation | null |
# nullt3r/Meta-Llama-3-8B-Instruct-64k-PoSE-Q8_0-GGUF
This model uses PoSE to extend Llama's context length from 8k to 64k (https://huggingface.co/winglian/Llama-3-8b-64k-PoSE). It performs exceptionally well when used with LM Studio and the standard LLaMA 3 profile. However, there is a notable issue with ollama—it continuously generates tokens without stopping.
This model was converted to GGUF format from [`Azma-AI/Meta-Llama-3-8B-Instruct-64k-PoSE`](https://huggingface.co/Azma-AI/Meta-Llama-3-8B-Instruct-64k-PoSE) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Azma-AI/Meta-Llama-3-8B-Instruct-64k-PoSE) for more details on the model.
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
| {"language": ["en"], "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"} | nullt3r/Meta-Llama-3-8B-Instruct-64k-PoSE-Q8_0-GGUF | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"region:us"
]
| null | 2024-04-25T18:55:13+00:00 |
null | null | {"license": "apache-2.0"} | LawAndOrderMatt/MACROSSOVER | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-25T18:57:22+00:00 |
|
null | null | {} | snakesss/Janika | null | [
"region:us"
]
| null | 2024-04-25T18:57:38+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
gemma-2b - bnb 8bits
- Model creator: https://huggingface.co/alpindale/
- Original model: https://huggingface.co/alpindale/gemma-2b/
Original model description:
---
library_name: transformers
tags: []
extra_gated_heading: "Access Gemma on Hugging Face"
extra_gated_prompt: "To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately."
extra_gated_button_content: "Acknowledge license"
---
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 2B base version of the Gemma model. You can also visit the model card of the [7B base model](https://huggingface.co/google/gemma-7b), [7B instruct model](https://huggingface.co/google/gemma-7b-it), and [2B instruct model](https://huggingface.co/google/gemma-2b-it).
**Resources and Technical Documentation**:
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
* [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent)
**Authors**: Google
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
They are text-to-text, decoder-only large language models, available in English,
with open weights, pre-trained variants, and instruction-tuned variants. Gemma
models are well-suited for a variety of text generation tasks, including
question answering, summarization, and reasoning. Their relatively small size
makes it possible to deploy them in environments with limited resources such as
a laptop, desktop or your own cloud infrastructure, democratizing access to
state of the art AI models and helping foster innovation for everyone.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase.
#### Fine-tuning the model
You can find fine-tuning scripts and notebook under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt it to this model, simply change the model-id to `google/gemma-2b`.
In that repository, we provide:
* A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA
* A script to perform SFT using FSDP on TPU devices
* A notebook that you can run on a free-tier Google Colab instance to perform SFT on English quotes dataset
#### Running the model on a CPU
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(**input_text, return_tensors="pt")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto")
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Running the model on a GPU using different precisions
* _Using `torch.float16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.float16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using `torch.bfloat16`_
```python
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Quantized Versions through `bitsandbytes`
* _Using 8-bit precision (int8)_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
* _Using 4-bit precision_
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b", quantization_config=quantization_config)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
```
#### Other optimizations
* _Flash Attention 2_
First make sure to install `flash-attn` in your environment `pip install flash-attn`
```diff
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
+ attn_implementation="flash_attention_2"
).to(0)
```
### Inputs and outputs
* **Input:** Text string, such as a question, a prompt, or a document to be
summarized.
* **Output:** Generated English-language text in response to the input, such
as an answer to a question, or a summary of a document.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources, totaling 6 trillion tokens. Here are the key components:
* Web Documents: A diverse collection of web text ensures the model is exposed
to a broad range of linguistic styles, topics, and vocabulary. Primarily
English-language content.
* Code: Exposing the model to code helps it to learn the syntax and patterns of
programming languages, which improves its ability to generate code or
understand code-related questions.
* Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
The combination of these diverse data sources is crucial for training a powerful
language model that can handle a wide variety of different tasks and text
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
* CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was
applied at multiple stages in the data preparation process to ensure the
exclusion of harmful and illegal content
* Sensitive Data Filtering: As part of making Gemma pre-trained models safe and
reliable, automated techniques were used to filter out certain personal
information and other sensitive data from training sets.
* Additional methods: Filtering based on content quality and safely in line with
[our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11).
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using the latest generation of
[Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e).
Training large language models requires significant computational power. TPUs,
designed specifically for matrix operations common in machine learning, offer
several advantages in this domain:
* Performance: TPUs are specifically designed to handle the massive computations
involved in training LLMs. They can speed up training considerably compared to
CPUs.
* Memory: TPUs often come with large amounts of high-bandwidth memory, allowing
for the handling of large models and batch sizes during training. This can
lead to better model quality.
* Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for
handling the growing complexity of large foundation models. You can distribute
training across multiple TPU devices for faster and more efficient processing.
* Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective
solution for training large models compared to CPU-based infrastructure,
especially when considering the time and resources saved due to faster
training.
* These advantages are aligned with
[Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways).
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models.
ML Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
[foundation models](https://ai.google/discover/foundation-models/), including large language models like
these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 |
| [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 |
| [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 |
| [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 59.7 | 51.8 |
| [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 |
| [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 |
| [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 |
| [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 |
| [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 |
| [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 |
| [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 |
| [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | - | 23 |
| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 |
| [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 |
| [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 |
| [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 |
| [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 |
| [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 |
| ------------------------------ | ------------- | ----------- | --------- |
| **Average** | | **54.0** | **56.4** |
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
* Text-to-Text Content Safety: Human evaluation on prompts covering safety
policies including child sexual abuse and exploitation, harassment, violence
and gore, and hate speech.
* Text-to-Text Representational Harms: Benchmark against relevant academic
datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2).
* Memorization: Automated evaluation of memorization of training data, including
the risk of personally identifiable information exposure.
* Large-scale harm: Tests for "dangerous capabilities," such as chemical,
biological, radiological, and nuclear (CBRN) risks.
### Evaluation Results
The results of ethics and safety evaluations are within acceptable thresholds
for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child
safety, content safety, representational harms, memorization, large-scale harms.
On top of robust internal evaluations, the results of well known safety
benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA
are shown here.
| Benchmark | Metric | 2B Params | 7B Params |
| ------------------------------ | ------------- | ----------- | --------- |
| [RealToxicity](https://arxiv.org/abs/2009.11462) | average | 6.86 | 7.90 |
| [BOLD](https://arxiv.org/abs/2101.11718) | | 45.57 | 49.08 |
| [CrowS-Pairs](https://aclanthology.org/2020.emnlp-main.154/) | top-1 | 45.82 | 51.33 |
| [BBQ Ambig](https://arxiv.org/abs/2110.08193v2) | 1-shot, top-1 | 62.58 | 92.54 |
| [BBQ Disambig](https://arxiv.org/abs/2110.08193v2) | top-1 | 54.62 | 71.99 |
| [Winogender](https://arxiv.org/abs/1804.09301) | top-1 | 51.25 | 54.17 |
| [TruthfulQA](https://arxiv.org/abs/2109.07958) | | 44.84 | 31.81 |
| [Winobias 1_2](https://arxiv.org/abs/1804.06876) | | 56.12 | 59.09 |
| [Winobias 2_2](https://arxiv.org/abs/1804.06876) | | 91.10 | 92.23 |
| [Toxigen](https://arxiv.org/abs/2203.09509) | | 29.77 | 39.59 |
| ------------------------------ | ------------- | ----------- | --------- |
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open Large Language Models (LLMs) have a wide range of applications across
various industries and domains. The following list of potential uses is not
comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
* Content Creation and Communication
* Text Generation: These models can be used to generate creative text formats
such as poems, scripts, code, marketing copy, and email drafts.
* Chatbots and Conversational AI: Power conversational interfaces for customer
service, virtual assistants, or interactive applications.
* Text Summarization: Generate concise summaries of a text corpus, research
papers, or reports.
* Research and Education
* Natural Language Processing (NLP) Research: These models can serve as a
foundation for researchers to experiment with NLP techniques, develop
algorithms, and contribute to the advancement of the field.
* Language Learning Tools: Support interactive language learning experiences,
aiding in grammar correction or providing writing practice.
* Knowledge Exploration: Assist researchers in exploring large bodies of text
by generating summaries or answering questions about specific topics.
### Limitations
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* Context and Task Complexity
* LLMs are better at tasks that can be framed with clear prompts and
instructions. Open-ended or highly complex tasks might be challenging.
* A model's performance can be influenced by the amount of context provided
(longer context generally leads to better outputs, up to a certain point).
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
At the time of release, this family of models provides high-performance open
large language model implementations designed from the ground up for Responsible
AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
| {} | RichardErkhov/alpindale_-_gemma-2b-8bits | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:2312.11805",
"arxiv:2009.03300",
"arxiv:1905.07830",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1905.10044",
"arxiv:1907.10641",
"arxiv:1811.00937",
"arxiv:1809.02789",
"arxiv:1911.01547",
"arxiv:1705.03551",
"arxiv:2107.03374",
"arxiv:2108.07732",
"arxiv:2110.14168",
"arxiv:2304.06364",
"arxiv:2206.04615",
"arxiv:1804.06876",
"arxiv:2110.08193",
"arxiv:2009.11462",
"arxiv:2101.11718",
"arxiv:1804.09301",
"arxiv:2109.07958",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-25T18:59:30+00:00 |
null | null | {} | jkorstad/wav2vec2-base-lang-id-finetuned-gtzan | null | [
"region:us"
]
| null | 2024-04-25T19:03:45+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** Kaizu07
- **License:** apache-2.0
- **Finetuned from model :** BanglaLLM/bangla-llama-7b-instruct-v0.1
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "BanglaLLM/bangla-llama-7b-instruct-v0.1"} | Kaizu07/llama2-bn-v0.2-16bit | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:BanglaLLM/bangla-llama-7b-instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:04:35+00:00 |
text-generation | transformers | {} | sidddddddddddd/Llama-2-7b-chat-finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:05:25+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/988fuap | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:05:26+00:00 |
null | null |
# NeuralsynthesisExperiment27pastiche-7B
NeuralsynthesisExperiment27pastiche-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: Kukedlc/NeuralSynthesis-7B-v0.1
- model: automerger/Experiment27Pastiche-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/NeuralsynthesisExperiment27pastiche-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/NeuralsynthesisExperiment27pastiche-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-25T19:06:29+00:00 |
text-generation | transformers |
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | chriztopherton/autotrain-raft-255P3 | null | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:07:31+00:00 |
text-generation | transformers | {} | wave-on-discord/llama-3-70b-llc-3-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:08:05+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** dbands
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | dbands/llama-3-8b-sql-instruct_16bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:09:48+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/ImagineIt/StoryTeller-70b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/StoryTeller-70b-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/StoryTeller-70b-i1-GGUF/resolve/main/StoryTeller-70b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "base_model": "ImagineIt/StoryTeller-70b", "quantized_by": "mradermacher"} | mradermacher/StoryTeller-70b-i1-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:ImagineIt/StoryTeller-70b",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:10:43+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | yxs33220/new_model_april_25 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:10:59+00:00 |
null | transformers |
```bash
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
git clone https://huggingface.co/muhtasham/llama3-ins-8b-int4-trt-llm
git clone https://github.com/NVIDIA/TensorRT-LLM.git
python ./TensorRT-LLM/examples/run.py --engine_dir=./ \
--max_output_len 5 \
--tokenizer_dir llama3-hf \
--input_text "How do I count to nine in French?" \
--run_profiling
2024-04-25 19:35:59.062455: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Input [Text 0]: "<|begin_of_text|>How do I count to nine in French?"
Output [Text 0 Beam 0]: " Counting in French is"
batch_size: 1, avg latency of 10 iterations: : 0.0999948501586914 sec
``` | {} | muhtasham/llama3-ins-8b-int4-trt-llm | null | [
"transformers",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:11:45+00:00 |
null | null | {"license": "apache-2.0"} | marstheonly/mars | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-25T19:12:00+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-chat-hf_fictional_arc_easy_english_v2
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 18
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "llama2", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "Llama-2-7b-chat-hf_fictional_arc_easy_english_v2", "results": []}]} | yzhuang/Llama-2-7b-chat-hf_fictional_arc_easy_english_v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:12:45+00:00 |
text2text-generation | transformers | {} | amrosama/my_awesome_opus_books_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:12:59+00:00 |
|
text-generation | transformers | Produced using https://github.com/neuralmagic/AutoFP8/blob/b0c1f789c51659bb023c06521ecbd04cea4a26f6/quantize.py
```bash
python quantize.py --model-id meta-llama/Meta-Llama-3-8B-Instruct --save-dir Meta-Llama-3-8B-Instruct-FP8
``` | {"tags": ["fp8"]} | nm-testing/Meta-Llama-3-8B-Instruct-FP8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"fp8",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:13:07+00:00 |
text-generation | transformers | # MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.1 AWQ
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [Llama-3-8B-Instruct-DPO-v0.1](https://huggingface.co/MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.1)
<img src="./llama-3-merges.webp" alt="Llama-3 DPO Logo" width="500" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
This model is a fine-tune (DPO) of `meta-llama/Meta-Llama-3-8B-Instruct` model.
## How to use
This model uses `ChatML` prompt template:
```
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
````
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Llama-3-8B-Instruct-DPO-v0.1-AWQ"
system_message = "You are Llama-3-8B-Instruct-DPO-v0.1, incarnated as a powerful AI. You were created by MaziyarPanahi."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### 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.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [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
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible", "axolotl", "finetune", "facebook", "meta", "pytorch", "llama", "llama-3"], "datasets": ["mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha"], "model_name": "Llama-3-8B-Instruct-DPO-v0.1", "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "inference": false, "model_creator": "MaziyarPanahi", "quantized_by": "Suparious"} | solidrust/Llama-3-8B-Instruct-DPO-v0.1-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"axolotl",
"finetune",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"dataset:mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha",
"license:other",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:13:53+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-base-finetuned-BBC
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2173
- Rouge1: 0.169
- Rouge2: 0.1419
- Rougel: 0.1624
- Rougelsum: 0.1651
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 1.0798 | 1.0 | 7 | 0.4261 | 0.1312 | 0.0749 | 0.0947 | 0.0967 |
| 0.4858 | 2.0 | 14 | 0.2775 | 0.1419 | 0.1037 | 0.1285 | 0.1288 |
| 0.3719 | 3.0 | 21 | 0.2435 | 0.16 | 0.1307 | 0.151 | 0.1523 |
| 0.298 | 4.0 | 28 | 0.2311 | 0.1619 | 0.1292 | 0.1527 | 0.1554 |
| 0.2607 | 5.0 | 35 | 0.2318 | 0.1593 | 0.1259 | 0.1493 | 0.1526 |
| 0.2276 | 6.0 | 42 | 0.2211 | 0.1566 | 0.1259 | 0.1479 | 0.151 |
| 0.2173 | 7.0 | 49 | 0.2177 | 0.169 | 0.1419 | 0.1624 | 0.1651 |
| 0.1801 | 8.0 | 56 | 0.2173 | 0.169 | 0.1419 | 0.1624 | 0.1651 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-finetuned-BBC", "results": []}]} | saikancharlareddy/bart-base-finetuned-BBC | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:14:02+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | himanshubeniwal/mbart-large-50-finetuned-kk-to-en-dumb-European | null | [
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:14:15+00:00 |
object-detection | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/qubvel-hf-co/transformers-detection-model-finetuning-cppe5/runs/y96s266q)
# facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3283
- Map: 0.2947
- Map 50: 0.5809
- Map 75: 0.2681
- Map Small: 0.1583
- Map Medium: 0.23
- Map Large: 0.4971
- Mar 1: 0.2972
- Mar 10: 0.4633
- Mar 100: 0.4771
- Mar Small: 0.2237
- Mar Medium: 0.4317
- Mar Large: 0.7008
- Map Coverall: 0.5445
- Mar 100 Coverall: 0.6829
- Map Face Shield: 0.2753
- Mar 100 Face Shield: 0.4937
- Map Gloves: 0.2028
- Mar 100 Gloves: 0.4098
- Map Goggles: 0.151
- Mar 100 Goggles: 0.3938
- Map Mask: 0.3002
- Mar 100 Mask: 0.4053
## 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: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| 2.7248 | 1.0 | 107 | 2.6074 | 0.0142 | 0.0396 | 0.0076 | 0.0039 | 0.0015 | 0.0139 | 0.0281 | 0.085 | 0.1226 | 0.0401 | 0.0831 | 0.1324 | 0.0625 | 0.3068 | 0.0 | 0.0 | 0.0024 | 0.1589 | 0.0 | 0.0 | 0.0061 | 0.1476 |
| 2.3311 | 2.0 | 214 | 2.3317 | 0.0296 | 0.0744 | 0.019 | 0.007 | 0.0084 | 0.0291 | 0.0548 | 0.1307 | 0.1676 | 0.0627 | 0.1019 | 0.1817 | 0.1282 | 0.4536 | 0.0 | 0.0 | 0.0076 | 0.1777 | 0.0 | 0.0 | 0.0119 | 0.2067 |
| 2.1268 | 3.0 | 321 | 2.2497 | 0.0259 | 0.0601 | 0.0189 | 0.0075 | 0.0188 | 0.0236 | 0.0516 | 0.1358 | 0.183 | 0.053 | 0.1058 | 0.2174 | 0.1028 | 0.5477 | 0.0 | 0.0 | 0.0056 | 0.175 | 0.0 | 0.0 | 0.0209 | 0.1924 |
| 1.9337 | 4.0 | 428 | 2.0126 | 0.0464 | 0.1088 | 0.0338 | 0.0173 | 0.0202 | 0.0392 | 0.0779 | 0.1704 | 0.2113 | 0.0536 | 0.1312 | 0.2662 | 0.166 | 0.5896 | 0.0 | 0.0 | 0.0117 | 0.2121 | 0.0 | 0.0 | 0.0543 | 0.2547 |
| 1.7712 | 5.0 | 535 | 1.9681 | 0.0654 | 0.1458 | 0.0466 | 0.0205 | 0.037 | 0.0593 | 0.0942 | 0.1996 | 0.236 | 0.0773 | 0.1439 | 0.3188 | 0.2289 | 0.6279 | 0.0026 | 0.0367 | 0.0318 | 0.2286 | 0.0 | 0.0 | 0.0636 | 0.2867 |
| 1.6867 | 6.0 | 642 | 1.9592 | 0.0736 | 0.1771 | 0.0487 | 0.0041 | 0.0415 | 0.1024 | 0.094 | 0.1794 | 0.2144 | 0.0524 | 0.1234 | 0.3006 | 0.2756 | 0.5964 | 0.0057 | 0.0342 | 0.0178 | 0.2353 | 0.0 | 0.0 | 0.0688 | 0.2062 |
| 1.6342 | 7.0 | 749 | 1.8956 | 0.0829 | 0.172 | 0.0727 | 0.0108 | 0.0426 | 0.131 | 0.1135 | 0.2153 | 0.2315 | 0.0463 | 0.1354 | 0.3658 | 0.2921 | 0.5892 | 0.0143 | 0.0873 | 0.0306 | 0.1817 | 0.002 | 0.0077 | 0.0758 | 0.2916 |
| 1.6003 | 8.0 | 856 | 1.7564 | 0.1105 | 0.2502 | 0.0854 | 0.0256 | 0.0815 | 0.1467 | 0.1344 | 0.2559 | 0.2844 | 0.074 | 0.2137 | 0.4119 | 0.3628 | 0.6252 | 0.0492 | 0.1987 | 0.036 | 0.2688 | 0.0009 | 0.0262 | 0.1035 | 0.3031 |
| 1.5536 | 9.0 | 963 | 1.8190 | 0.1043 | 0.2518 | 0.0728 | 0.0168 | 0.0669 | 0.1254 | 0.1301 | 0.2492 | 0.268 | 0.0637 | 0.1812 | 0.4043 | 0.3236 | 0.5775 | 0.0381 | 0.1823 | 0.043 | 0.2424 | 0.0011 | 0.0569 | 0.1158 | 0.2809 |
| 1.5082 | 10.0 | 1070 | 1.7315 | 0.1314 | 0.2986 | 0.1032 | 0.0233 | 0.0862 | 0.1886 | 0.1654 | 0.2975 | 0.3201 | 0.062 | 0.2453 | 0.4825 | 0.3749 | 0.5991 | 0.0633 | 0.2696 | 0.0546 | 0.2728 | 0.0197 | 0.1569 | 0.1446 | 0.3022 |
| 1.4375 | 11.0 | 1177 | 1.6288 | 0.1535 | 0.3396 | 0.1235 | 0.0308 | 0.1048 | 0.2523 | 0.1912 | 0.3504 | 0.3762 | 0.1132 | 0.281 | 0.5931 | 0.4365 | 0.65 | 0.0788 | 0.3456 | 0.0635 | 0.2848 | 0.02 | 0.2708 | 0.1687 | 0.3298 |
| 1.4056 | 12.0 | 1284 | 1.6457 | 0.1394 | 0.3148 | 0.0984 | 0.0325 | 0.0954 | 0.2416 | 0.1685 | 0.3278 | 0.3516 | 0.0839 | 0.2689 | 0.5579 | 0.3979 | 0.6477 | 0.0657 | 0.2861 | 0.0692 | 0.2688 | 0.0096 | 0.2615 | 0.1546 | 0.2938 |
| 1.424 | 13.0 | 1391 | 1.6102 | 0.1626 | 0.3626 | 0.1216 | 0.0531 | 0.1099 | 0.2685 | 0.1902 | 0.3443 | 0.3722 | 0.1117 | 0.299 | 0.5444 | 0.4262 | 0.6423 | 0.0721 | 0.3519 | 0.0749 | 0.283 | 0.0412 | 0.2585 | 0.1984 | 0.3253 |
| 1.3553 | 14.0 | 1498 | 1.5945 | 0.1601 | 0.3462 | 0.1318 | 0.0314 | 0.1071 | 0.278 | 0.1977 | 0.3453 | 0.3659 | 0.1093 | 0.2915 | 0.5759 | 0.4558 | 0.6329 | 0.0692 | 0.3291 | 0.0696 | 0.2937 | 0.0229 | 0.2554 | 0.183 | 0.3182 |
| 1.3127 | 15.0 | 1605 | 1.6288 | 0.165 | 0.3566 | 0.1315 | 0.0444 | 0.1033 | 0.287 | 0.1937 | 0.3491 | 0.3718 | 0.0778 | 0.2843 | 0.6119 | 0.4463 | 0.6383 | 0.0903 | 0.343 | 0.0818 | 0.3049 | 0.0277 | 0.2569 | 0.1791 | 0.316 |
| 1.2941 | 16.0 | 1712 | 1.5854 | 0.1643 | 0.3635 | 0.1281 | 0.0533 | 0.1198 | 0.2617 | 0.2003 | 0.3668 | 0.3896 | 0.1619 | 0.3128 | 0.5948 | 0.439 | 0.641 | 0.1011 | 0.3886 | 0.0718 | 0.2973 | 0.0277 | 0.3123 | 0.1819 | 0.3089 |
| 1.271 | 17.0 | 1819 | 1.5453 | 0.1645 | 0.3585 | 0.1352 | 0.069 | 0.1089 | 0.2721 | 0.2053 | 0.3563 | 0.3835 | 0.1498 | 0.3023 | 0.5795 | 0.4413 | 0.65 | 0.0907 | 0.3405 | 0.0652 | 0.2969 | 0.0292 | 0.3092 | 0.1963 | 0.3209 |
| 1.2797 | 18.0 | 1926 | 1.4980 | 0.1828 | 0.3932 | 0.1529 | 0.0898 | 0.1312 | 0.3023 | 0.2189 | 0.3885 | 0.4139 | 0.2014 | 0.3349 | 0.6208 | 0.4426 | 0.6338 | 0.1163 | 0.4 | 0.085 | 0.3286 | 0.05 | 0.3585 | 0.2203 | 0.3489 |
| 1.2202 | 19.0 | 2033 | 1.5525 | 0.1768 | 0.3837 | 0.1496 | 0.0654 | 0.1163 | 0.3329 | 0.2189 | 0.3765 | 0.4027 | 0.1528 | 0.3314 | 0.6205 | 0.4413 | 0.6171 | 0.1071 | 0.3975 | 0.0922 | 0.329 | 0.0324 | 0.3277 | 0.211 | 0.3422 |
| 1.2601 | 20.0 | 2140 | 1.5374 | 0.1806 | 0.3936 | 0.1454 | 0.0636 | 0.1232 | 0.2955 | 0.2168 | 0.373 | 0.401 | 0.1507 | 0.3136 | 0.6096 | 0.4367 | 0.6324 | 0.1267 | 0.4101 | 0.0865 | 0.2737 | 0.0448 | 0.3446 | 0.2084 | 0.344 |
| 1.2382 | 21.0 | 2247 | 1.5249 | 0.1687 | 0.3792 | 0.1313 | 0.0512 | 0.1189 | 0.3014 | 0.2056 | 0.3703 | 0.394 | 0.1416 | 0.3245 | 0.5803 | 0.4207 | 0.6266 | 0.0906 | 0.3886 | 0.073 | 0.3022 | 0.0526 | 0.3231 | 0.2066 | 0.3293 |
| 1.1701 | 22.0 | 2354 | 1.5312 | 0.1891 | 0.4048 | 0.1572 | 0.0608 | 0.1315 | 0.3261 | 0.2291 | 0.3843 | 0.4069 | 0.135 | 0.3326 | 0.6264 | 0.4435 | 0.6234 | 0.1407 | 0.3684 | 0.0997 | 0.35 | 0.0451 | 0.3492 | 0.2168 | 0.3436 |
| 1.1604 | 23.0 | 2461 | 1.4588 | 0.1924 | 0.4116 | 0.1591 | 0.0521 | 0.1244 | 0.3417 | 0.2199 | 0.3947 | 0.4099 | 0.1433 | 0.3346 | 0.6199 | 0.4795 | 0.6293 | 0.1222 | 0.381 | 0.0932 | 0.3335 | 0.0501 | 0.3508 | 0.2167 | 0.3551 |
| 1.1605 | 24.0 | 2568 | 1.4838 | 0.1938 | 0.4038 | 0.159 | 0.0595 | 0.1262 | 0.3412 | 0.2126 | 0.3878 | 0.4032 | 0.153 | 0.3075 | 0.6215 | 0.4689 | 0.6419 | 0.1383 | 0.3848 | 0.1099 | 0.354 | 0.0458 | 0.3092 | 0.2061 | 0.3262 |
| 1.1148 | 25.0 | 2675 | 1.4525 | 0.19 | 0.3952 | 0.1518 | 0.0459 | 0.1462 | 0.3308 | 0.2158 | 0.3855 | 0.4048 | 0.1365 | 0.3526 | 0.591 | 0.4618 | 0.6446 | 0.1184 | 0.4063 | 0.0995 | 0.35 | 0.0523 | 0.2862 | 0.218 | 0.3369 |
| 1.1126 | 26.0 | 2782 | 1.4628 | 0.2043 | 0.4292 | 0.1697 | 0.0581 | 0.1509 | 0.3362 | 0.234 | 0.403 | 0.4236 | 0.1471 | 0.3592 | 0.6312 | 0.4892 | 0.6635 | 0.1204 | 0.3924 | 0.1185 | 0.3442 | 0.0491 | 0.3692 | 0.2444 | 0.3489 |
| 1.1128 | 27.0 | 2889 | 1.4258 | 0.2041 | 0.4284 | 0.1715 | 0.0714 | 0.1429 | 0.3312 | 0.232 | 0.4125 | 0.4299 | 0.1504 | 0.3629 | 0.6398 | 0.4813 | 0.6635 | 0.1375 | 0.4392 | 0.1243 | 0.3491 | 0.0419 | 0.3292 | 0.2358 | 0.3684 |
| 1.0908 | 28.0 | 2996 | 1.4615 | 0.2072 | 0.425 | 0.1828 | 0.0839 | 0.1465 | 0.3402 | 0.2404 | 0.3906 | 0.4027 | 0.1514 | 0.3253 | 0.6275 | 0.4933 | 0.6482 | 0.1083 | 0.3886 | 0.1146 | 0.3339 | 0.0681 | 0.2908 | 0.2517 | 0.352 |
| 1.0785 | 29.0 | 3103 | 1.4452 | 0.195 | 0.4186 | 0.1581 | 0.0527 | 0.1553 | 0.3474 | 0.2285 | 0.3948 | 0.4135 | 0.1668 | 0.3468 | 0.6451 | 0.4759 | 0.6572 | 0.108 | 0.4152 | 0.1055 | 0.3357 | 0.0649 | 0.32 | 0.2207 | 0.3396 |
| 1.0677 | 30.0 | 3210 | 1.4368 | 0.2105 | 0.4321 | 0.187 | 0.0744 | 0.1641 | 0.3346 | 0.243 | 0.4065 | 0.4272 | 0.1789 | 0.3693 | 0.6408 | 0.4924 | 0.6662 | 0.1364 | 0.4152 | 0.1271 | 0.3388 | 0.0754 | 0.3785 | 0.2213 | 0.3373 |
| 1.0448 | 31.0 | 3317 | 1.4151 | 0.2115 | 0.436 | 0.1687 | 0.063 | 0.1549 | 0.3449 | 0.2306 | 0.4104 | 0.4299 | 0.1689 | 0.3668 | 0.6302 | 0.4999 | 0.6423 | 0.1308 | 0.4304 | 0.1373 | 0.3554 | 0.0478 | 0.3615 | 0.2418 | 0.36 |
| 1.0656 | 32.0 | 3424 | 1.4272 | 0.2218 | 0.449 | 0.1816 | 0.0807 | 0.1638 | 0.3717 | 0.2564 | 0.4207 | 0.4405 | 0.1786 | 0.3802 | 0.6742 | 0.4992 | 0.6563 | 0.1638 | 0.4633 | 0.1285 | 0.35 | 0.0649 | 0.3631 | 0.2528 | 0.3698 |
| 1.0345 | 33.0 | 3531 | 1.4501 | 0.2144 | 0.4477 | 0.174 | 0.0843 | 0.1471 | 0.3437 | 0.242 | 0.4054 | 0.43 | 0.1869 | 0.3499 | 0.6493 | 0.5021 | 0.645 | 0.1414 | 0.438 | 0.1352 | 0.3585 | 0.0702 | 0.3585 | 0.2233 | 0.3502 |
| 1.0243 | 34.0 | 3638 | 1.3969 | 0.2248 | 0.4842 | 0.1808 | 0.0848 | 0.1805 | 0.371 | 0.2491 | 0.4226 | 0.4434 | 0.1466 | 0.405 | 0.6755 | 0.4944 | 0.6721 | 0.1569 | 0.4443 | 0.1482 | 0.3634 | 0.0832 | 0.3754 | 0.2412 | 0.3618 |
| 1.0221 | 35.0 | 3745 | 1.4094 | 0.2203 | 0.4537 | 0.1956 | 0.0682 | 0.1655 | 0.3699 | 0.2469 | 0.4174 | 0.4359 | 0.1675 | 0.3785 | 0.6632 | 0.5107 | 0.6613 | 0.1535 | 0.4405 | 0.1419 | 0.3705 | 0.0566 | 0.3462 | 0.239 | 0.3609 |
| 0.99 | 36.0 | 3852 | 1.3827 | 0.2092 | 0.4456 | 0.1798 | 0.0706 | 0.1619 | 0.3708 | 0.2594 | 0.4185 | 0.4385 | 0.1516 | 0.39 | 0.6637 | 0.4897 | 0.6536 | 0.1332 | 0.4165 | 0.154 | 0.3674 | 0.057 | 0.4031 | 0.2119 | 0.352 |
| 0.9819 | 37.0 | 3959 | 1.4144 | 0.2298 | 0.4652 | 0.1873 | 0.0827 | 0.1817 | 0.3784 | 0.2504 | 0.4264 | 0.4448 | 0.1796 | 0.3827 | 0.68 | 0.5135 | 0.6743 | 0.1888 | 0.4392 | 0.1466 | 0.3714 | 0.0676 | 0.3769 | 0.2325 | 0.3622 |
| 0.9652 | 38.0 | 4066 | 1.3730 | 0.2336 | 0.487 | 0.2047 | 0.0886 | 0.1914 | 0.3765 | 0.2472 | 0.4256 | 0.4448 | 0.1851 | 0.3925 | 0.6692 | 0.5115 | 0.6743 | 0.1967 | 0.4544 | 0.1575 | 0.3759 | 0.0675 | 0.3554 | 0.235 | 0.364 |
| 0.9397 | 39.0 | 4173 | 1.3323 | 0.2396 | 0.4804 | 0.215 | 0.1072 | 0.1857 | 0.4298 | 0.2672 | 0.4452 | 0.4634 | 0.1984 | 0.3967 | 0.7004 | 0.5192 | 0.6806 | 0.1547 | 0.4785 | 0.1653 | 0.3848 | 0.1059 | 0.3908 | 0.2531 | 0.3822 |
| 0.9346 | 40.0 | 4280 | 1.3810 | 0.2354 | 0.488 | 0.2112 | 0.1037 | 0.1849 | 0.4009 | 0.2623 | 0.4279 | 0.4404 | 0.1807 | 0.3652 | 0.6944 | 0.51 | 0.6788 | 0.1698 | 0.4456 | 0.1541 | 0.3509 | 0.098 | 0.3569 | 0.245 | 0.3698 |
| 0.9575 | 41.0 | 4387 | 1.3592 | 0.2396 | 0.4808 | 0.2072 | 0.1173 | 0.2005 | 0.373 | 0.2488 | 0.4339 | 0.4533 | 0.2073 | 0.4023 | 0.6733 | 0.5255 | 0.6878 | 0.1735 | 0.4443 | 0.1591 | 0.383 | 0.1032 | 0.3862 | 0.2368 | 0.3653 |
| 0.948 | 42.0 | 4494 | 1.3716 | 0.2295 | 0.4945 | 0.1711 | 0.0793 | 0.1755 | 0.3977 | 0.2508 | 0.4201 | 0.4391 | 0.1373 | 0.3812 | 0.6851 | 0.5015 | 0.6644 | 0.1825 | 0.4443 | 0.1456 | 0.3696 | 0.094 | 0.3662 | 0.2238 | 0.3511 |
| 0.9254 | 43.0 | 4601 | 1.3677 | 0.238 | 0.4902 | 0.2091 | 0.1018 | 0.1861 | 0.4061 | 0.2651 | 0.4344 | 0.4542 | 0.1866 | 0.389 | 0.6789 | 0.5221 | 0.6968 | 0.1681 | 0.4481 | 0.1749 | 0.4013 | 0.0885 | 0.3677 | 0.2363 | 0.3569 |
| 0.9162 | 44.0 | 4708 | 1.4004 | 0.2363 | 0.491 | 0.2079 | 0.0949 | 0.1897 | 0.394 | 0.2511 | 0.4205 | 0.4403 | 0.1717 | 0.391 | 0.6663 | 0.5106 | 0.6919 | 0.1837 | 0.4241 | 0.1573 | 0.3732 | 0.0733 | 0.3385 | 0.2566 | 0.3738 |
| 0.9186 | 45.0 | 4815 | 1.3953 | 0.2378 | 0.4922 | 0.2211 | 0.1115 | 0.1843 | 0.3661 | 0.2527 | 0.4224 | 0.4439 | 0.2003 | 0.3923 | 0.6378 | 0.4989 | 0.6761 | 0.2 | 0.4418 | 0.1678 | 0.3821 | 0.0843 | 0.3677 | 0.238 | 0.3516 |
| 0.9225 | 46.0 | 4922 | 1.3936 | 0.2395 | 0.5114 | 0.1973 | 0.1111 | 0.1805 | 0.405 | 0.2557 | 0.4384 | 0.4586 | 0.2039 | 0.3929 | 0.6808 | 0.4977 | 0.6716 | 0.1804 | 0.4595 | 0.1716 | 0.3933 | 0.0927 | 0.4062 | 0.2553 | 0.3622 |
| 0.9011 | 47.0 | 5029 | 1.3632 | 0.2437 | 0.4962 | 0.2148 | 0.0766 | 0.1902 | 0.4155 | 0.2687 | 0.4318 | 0.452 | 0.1849 | 0.4063 | 0.6736 | 0.528 | 0.6703 | 0.1841 | 0.4696 | 0.1692 | 0.3772 | 0.0796 | 0.3754 | 0.2579 | 0.3676 |
| 0.8909 | 48.0 | 5136 | 1.3843 | 0.2513 | 0.5148 | 0.211 | 0.1097 | 0.1896 | 0.4229 | 0.2602 | 0.427 | 0.4438 | 0.1835 | 0.3919 | 0.6837 | 0.5279 | 0.6649 | 0.1892 | 0.4165 | 0.168 | 0.3862 | 0.1023 | 0.3677 | 0.2692 | 0.384 |
| 0.9073 | 49.0 | 5243 | 1.3763 | 0.2411 | 0.4936 | 0.208 | 0.1217 | 0.1832 | 0.4036 | 0.2703 | 0.4364 | 0.4507 | 0.2241 | 0.3758 | 0.6765 | 0.5104 | 0.6468 | 0.18 | 0.4747 | 0.1656 | 0.379 | 0.0975 | 0.3646 | 0.2522 | 0.3884 |
| 0.8877 | 50.0 | 5350 | 1.3689 | 0.251 | 0.5232 | 0.2096 | 0.1109 | 0.1933 | 0.4366 | 0.2773 | 0.4407 | 0.4563 | 0.208 | 0.3948 | 0.7056 | 0.526 | 0.6712 | 0.1895 | 0.4532 | 0.1796 | 0.392 | 0.106 | 0.3831 | 0.254 | 0.3822 |
| 0.8917 | 51.0 | 5457 | 1.3656 | 0.2506 | 0.51 | 0.202 | 0.1155 | 0.1989 | 0.4294 | 0.2728 | 0.4417 | 0.4633 | 0.2124 | 0.4163 | 0.6978 | 0.524 | 0.6797 | 0.1921 | 0.4519 | 0.1789 | 0.3924 | 0.0954 | 0.3954 | 0.2627 | 0.3969 |
| 0.8844 | 52.0 | 5564 | 1.3813 | 0.249 | 0.5001 | 0.2201 | 0.0869 | 0.1916 | 0.4365 | 0.253 | 0.4423 | 0.4577 | 0.2158 | 0.3852 | 0.696 | 0.5307 | 0.6829 | 0.1786 | 0.4595 | 0.1711 | 0.3607 | 0.0967 | 0.3892 | 0.2677 | 0.396 |
| 0.8548 | 53.0 | 5671 | 1.3952 | 0.2509 | 0.5076 | 0.2131 | 0.0846 | 0.1989 | 0.4249 | 0.2738 | 0.4475 | 0.4634 | 0.1904 | 0.4007 | 0.708 | 0.5228 | 0.6694 | 0.2054 | 0.4785 | 0.1833 | 0.3817 | 0.0732 | 0.3908 | 0.27 | 0.3964 |
| 0.8677 | 54.0 | 5778 | 1.4126 | 0.2542 | 0.5102 | 0.2243 | 0.1042 | 0.1943 | 0.4266 | 0.2703 | 0.4432 | 0.464 | 0.2001 | 0.4123 | 0.7115 | 0.5149 | 0.6689 | 0.2095 | 0.4797 | 0.1891 | 0.3973 | 0.0973 | 0.3908 | 0.2605 | 0.3831 |
| 0.8411 | 55.0 | 5885 | 1.3719 | 0.2622 | 0.5302 | 0.2162 | 0.1064 | 0.1973 | 0.4546 | 0.2722 | 0.4387 | 0.4582 | 0.2055 | 0.4015 | 0.6793 | 0.5294 | 0.6721 | 0.2128 | 0.4658 | 0.1936 | 0.3853 | 0.1067 | 0.38 | 0.2684 | 0.388 |
| 0.8304 | 56.0 | 5992 | 1.3720 | 0.2574 | 0.5284 | 0.2123 | 0.111 | 0.2098 | 0.4288 | 0.2714 | 0.4422 | 0.4608 | 0.2192 | 0.4254 | 0.678 | 0.513 | 0.673 | 0.2099 | 0.457 | 0.1897 | 0.3884 | 0.1077 | 0.3908 | 0.2668 | 0.3947 |
| 0.8494 | 57.0 | 6099 | 1.3436 | 0.2688 | 0.5468 | 0.2296 | 0.1174 | 0.2014 | 0.487 | 0.2786 | 0.4496 | 0.4712 | 0.2191 | 0.4218 | 0.7035 | 0.5323 | 0.6797 | 0.2373 | 0.4949 | 0.1761 | 0.3746 | 0.1257 | 0.4169 | 0.2728 | 0.3898 |
| 0.8505 | 58.0 | 6206 | 1.3279 | 0.2665 | 0.522 | 0.237 | 0.1173 | 0.2102 | 0.4605 | 0.2776 | 0.4479 | 0.4679 | 0.2063 | 0.4194 | 0.6983 | 0.5201 | 0.6833 | 0.2246 | 0.4772 | 0.1797 | 0.3857 | 0.1267 | 0.3954 | 0.2814 | 0.3978 |
| 0.8227 | 59.0 | 6313 | 1.3279 | 0.2668 | 0.5267 | 0.2222 | 0.1304 | 0.208 | 0.4523 | 0.2823 | 0.4514 | 0.4696 | 0.223 | 0.4233 | 0.6969 | 0.5244 | 0.6757 | 0.2493 | 0.5089 | 0.1784 | 0.3982 | 0.1102 | 0.3723 | 0.2717 | 0.3929 |
| 0.8129 | 60.0 | 6420 | 1.3400 | 0.2673 | 0.5348 | 0.2388 | 0.1185 | 0.2189 | 0.4501 | 0.2807 | 0.4554 | 0.4708 | 0.2163 | 0.4207 | 0.7048 | 0.5286 | 0.6761 | 0.2222 | 0.4646 | 0.1809 | 0.3969 | 0.1413 | 0.4231 | 0.2635 | 0.3933 |
| 0.8054 | 61.0 | 6527 | 1.3815 | 0.2734 | 0.548 | 0.2313 | 0.1199 | 0.2321 | 0.4526 | 0.282 | 0.4534 | 0.4687 | 0.2029 | 0.427 | 0.6887 | 0.5306 | 0.6797 | 0.2432 | 0.4734 | 0.1897 | 0.3942 | 0.1207 | 0.4 | 0.2825 | 0.396 |
| 0.7911 | 62.0 | 6634 | 1.3294 | 0.2704 | 0.5431 | 0.2301 | 0.1107 | 0.2143 | 0.4745 | 0.2791 | 0.4557 | 0.4704 | 0.2158 | 0.4193 | 0.7055 | 0.5363 | 0.691 | 0.2281 | 0.4532 | 0.1879 | 0.3929 | 0.1242 | 0.4138 | 0.2756 | 0.4013 |
| 0.7883 | 63.0 | 6741 | 1.3769 | 0.2605 | 0.5374 | 0.2202 | 0.1197 | 0.2211 | 0.4287 | 0.2717 | 0.4422 | 0.459 | 0.2065 | 0.4214 | 0.7033 | 0.5196 | 0.6698 | 0.2206 | 0.4646 | 0.176 | 0.3696 | 0.1092 | 0.4108 | 0.2772 | 0.3804 |
| 0.786 | 64.0 | 6848 | 1.3379 | 0.2666 | 0.5441 | 0.2257 | 0.1268 | 0.2197 | 0.468 | 0.2779 | 0.4595 | 0.479 | 0.2326 | 0.4344 | 0.7088 | 0.5226 | 0.6779 | 0.2319 | 0.5076 | 0.172 | 0.3933 | 0.1309 | 0.4169 | 0.2755 | 0.3991 |
| 0.7776 | 65.0 | 6955 | 1.3192 | 0.2708 | 0.5498 | 0.2165 | 0.1242 | 0.2149 | 0.4638 | 0.2795 | 0.4604 | 0.4736 | 0.2291 | 0.4245 | 0.6993 | 0.5326 | 0.6761 | 0.2312 | 0.4823 | 0.194 | 0.3951 | 0.1255 | 0.4138 | 0.2704 | 0.4004 |
| 0.7615 | 66.0 | 7062 | 1.3282 | 0.2745 | 0.5488 | 0.2276 | 0.1299 | 0.2271 | 0.459 | 0.2828 | 0.458 | 0.4734 | 0.2233 | 0.4263 | 0.695 | 0.5327 | 0.6725 | 0.2393 | 0.4949 | 0.1862 | 0.3946 | 0.1283 | 0.4015 | 0.2862 | 0.4036 |
| 0.7625 | 67.0 | 7169 | 1.3395 | 0.2778 | 0.5506 | 0.2384 | 0.1129 | 0.2216 | 0.4549 | 0.2754 | 0.4565 | 0.4698 | 0.2099 | 0.4264 | 0.7016 | 0.5473 | 0.6829 | 0.2375 | 0.4835 | 0.1937 | 0.3866 | 0.1235 | 0.3938 | 0.2872 | 0.4022 |
| 0.7495 | 68.0 | 7276 | 1.3261 | 0.2763 | 0.5487 | 0.2482 | 0.1388 | 0.2211 | 0.4822 | 0.2841 | 0.4541 | 0.4678 | 0.2334 | 0.4186 | 0.6913 | 0.5344 | 0.6752 | 0.2438 | 0.4633 | 0.1956 | 0.4027 | 0.12 | 0.3954 | 0.2876 | 0.4027 |
| 0.752 | 69.0 | 7383 | 1.3089 | 0.2816 | 0.5614 | 0.2464 | 0.1287 | 0.2309 | 0.4715 | 0.2833 | 0.4535 | 0.4704 | 0.2142 | 0.4297 | 0.6813 | 0.5332 | 0.6775 | 0.2508 | 0.4785 | 0.199 | 0.4062 | 0.1332 | 0.3831 | 0.2915 | 0.4067 |
| 0.7329 | 70.0 | 7490 | 1.3402 | 0.2703 | 0.5482 | 0.2299 | 0.1397 | 0.2188 | 0.459 | 0.2748 | 0.4504 | 0.4671 | 0.2478 | 0.4077 | 0.6824 | 0.5322 | 0.6788 | 0.2376 | 0.4544 | 0.181 | 0.3955 | 0.1111 | 0.3985 | 0.2895 | 0.4084 |
| 0.7383 | 71.0 | 7597 | 1.3367 | 0.2789 | 0.559 | 0.2514 | 0.1478 | 0.2189 | 0.4587 | 0.2852 | 0.462 | 0.4785 | 0.2426 | 0.4202 | 0.6802 | 0.5409 | 0.6928 | 0.2435 | 0.4595 | 0.1959 | 0.4 | 0.1287 | 0.4262 | 0.2855 | 0.4142 |
| 0.7223 | 72.0 | 7704 | 1.3356 | 0.2774 | 0.5589 | 0.2312 | 0.1569 | 0.2185 | 0.4519 | 0.2852 | 0.4558 | 0.4692 | 0.2374 | 0.4173 | 0.6811 | 0.5452 | 0.6883 | 0.2422 | 0.4646 | 0.1951 | 0.3888 | 0.1131 | 0.3923 | 0.2915 | 0.412 |
| 0.7155 | 73.0 | 7811 | 1.3295 | 0.2745 | 0.5553 | 0.2355 | 0.1444 | 0.2142 | 0.4732 | 0.2876 | 0.4614 | 0.4763 | 0.2288 | 0.4301 | 0.6877 | 0.5413 | 0.6851 | 0.2356 | 0.4835 | 0.1922 | 0.4013 | 0.1264 | 0.4077 | 0.2768 | 0.404 |
| 0.7123 | 74.0 | 7918 | 1.3261 | 0.2725 | 0.546 | 0.2292 | 0.1447 | 0.2054 | 0.464 | 0.2856 | 0.4561 | 0.4731 | 0.2266 | 0.4088 | 0.695 | 0.542 | 0.6779 | 0.2295 | 0.4696 | 0.186 | 0.4138 | 0.121 | 0.4062 | 0.2842 | 0.3978 |
| 0.7044 | 75.0 | 8025 | 1.3641 | 0.274 | 0.5524 | 0.2362 | 0.1446 | 0.2029 | 0.4664 | 0.29 | 0.4518 | 0.4683 | 0.2221 | 0.416 | 0.6934 | 0.5351 | 0.6761 | 0.2387 | 0.481 | 0.1923 | 0.3933 | 0.1144 | 0.3969 | 0.2895 | 0.3942 |
| 0.6946 | 76.0 | 8132 | 1.3353 | 0.2769 | 0.5585 | 0.2302 | 0.1549 | 0.2175 | 0.457 | 0.2855 | 0.4546 | 0.4677 | 0.221 | 0.4138 | 0.6847 | 0.5438 | 0.6833 | 0.2514 | 0.4709 | 0.1899 | 0.392 | 0.1209 | 0.4 | 0.2785 | 0.3924 |
| 0.692 | 77.0 | 8239 | 1.3378 | 0.2818 | 0.5631 | 0.2419 | 0.1771 | 0.2198 | 0.4555 | 0.2929 | 0.4592 | 0.4761 | 0.2936 | 0.4141 | 0.6873 | 0.5437 | 0.6815 | 0.2651 | 0.4899 | 0.2011 | 0.4062 | 0.1198 | 0.4108 | 0.2795 | 0.392 |
| 0.6912 | 78.0 | 8346 | 1.3104 | 0.2801 | 0.5626 | 0.2337 | 0.1692 | 0.2139 | 0.4725 | 0.2856 | 0.4588 | 0.476 | 0.2346 | 0.4302 | 0.6936 | 0.5434 | 0.6919 | 0.2599 | 0.462 | 0.1845 | 0.4071 | 0.1339 | 0.42 | 0.2787 | 0.3991 |
| 0.6841 | 79.0 | 8453 | 1.3479 | 0.2835 | 0.568 | 0.2334 | 0.1536 | 0.2137 | 0.483 | 0.2937 | 0.4524 | 0.4712 | 0.2171 | 0.4099 | 0.7032 | 0.5365 | 0.6811 | 0.2659 | 0.4709 | 0.1912 | 0.4027 | 0.1389 | 0.4108 | 0.2852 | 0.3907 |
| 0.6772 | 80.0 | 8560 | 1.3513 | 0.2853 | 0.5623 | 0.2438 | 0.1542 | 0.2224 | 0.4758 | 0.2944 | 0.4557 | 0.4709 | 0.2264 | 0.4131 | 0.7013 | 0.5309 | 0.6784 | 0.266 | 0.4671 | 0.1991 | 0.4103 | 0.1429 | 0.4 | 0.2877 | 0.3987 |
| 0.6748 | 81.0 | 8667 | 1.3345 | 0.2853 | 0.5691 | 0.2438 | 0.1607 | 0.2242 | 0.4818 | 0.2931 | 0.4543 | 0.469 | 0.2335 | 0.421 | 0.6924 | 0.5383 | 0.6851 | 0.2584 | 0.4519 | 0.2001 | 0.4031 | 0.1403 | 0.4 | 0.2894 | 0.4049 |
| 0.6601 | 82.0 | 8774 | 1.3315 | 0.291 | 0.574 | 0.2597 | 0.1627 | 0.2333 | 0.4837 | 0.2899 | 0.4605 | 0.4754 | 0.2354 | 0.4348 | 0.6868 | 0.5364 | 0.6797 | 0.2761 | 0.4797 | 0.1991 | 0.404 | 0.143 | 0.4031 | 0.3003 | 0.4102 |
| 0.6598 | 83.0 | 8881 | 1.3230 | 0.2917 | 0.5794 | 0.2537 | 0.1385 | 0.2287 | 0.4916 | 0.2912 | 0.4621 | 0.477 | 0.2305 | 0.4339 | 0.6913 | 0.5473 | 0.6892 | 0.2669 | 0.4747 | 0.1975 | 0.4205 | 0.1506 | 0.3938 | 0.296 | 0.4067 |
| 0.6577 | 84.0 | 8988 | 1.3283 | 0.2947 | 0.5809 | 0.2681 | 0.1583 | 0.23 | 0.4971 | 0.2972 | 0.4633 | 0.4771 | 0.2237 | 0.4317 | 0.7008 | 0.5445 | 0.6829 | 0.2753 | 0.4937 | 0.2028 | 0.4098 | 0.151 | 0.3938 | 0.3002 | 0.4053 |
| 0.6657 | 85.0 | 9095 | 1.3270 | 0.292 | 0.5805 | 0.2547 | 0.1669 | 0.224 | 0.4878 | 0.2968 | 0.46 | 0.4757 | 0.2361 | 0.4245 | 0.699 | 0.5422 | 0.6865 | 0.2658 | 0.4696 | 0.2014 | 0.4107 | 0.1552 | 0.4 | 0.2954 | 0.4116 |
| 0.6451 | 86.0 | 9202 | 1.3250 | 0.2898 | 0.5695 | 0.2528 | 0.1706 | 0.2256 | 0.4978 | 0.2932 | 0.4548 | 0.4709 | 0.225 | 0.4229 | 0.6988 | 0.5352 | 0.6766 | 0.2675 | 0.4797 | 0.1967 | 0.396 | 0.1485 | 0.3938 | 0.3011 | 0.4084 |
| 0.6501 | 87.0 | 9309 | 1.3576 | 0.2934 | 0.5824 | 0.2498 | 0.1556 | 0.2243 | 0.4893 | 0.2938 | 0.4633 | 0.4791 | 0.2446 | 0.4291 | 0.702 | 0.5461 | 0.6847 | 0.2651 | 0.4899 | 0.2 | 0.3969 | 0.1569 | 0.4108 | 0.2989 | 0.4133 |
| 0.6444 | 88.0 | 9416 | 1.3638 | 0.2911 | 0.5818 | 0.2514 | 0.1715 | 0.2217 | 0.4913 | 0.2945 | 0.4576 | 0.4745 | 0.2477 | 0.4226 | 0.6918 | 0.5451 | 0.6788 | 0.2577 | 0.4835 | 0.2045 | 0.4049 | 0.1554 | 0.3969 | 0.2929 | 0.4084 |
| 0.6275 | 89.0 | 9523 | 1.3529 | 0.2908 | 0.5777 | 0.2417 | 0.1722 | 0.2221 | 0.4779 | 0.2948 | 0.4575 | 0.4722 | 0.2504 | 0.4186 | 0.6857 | 0.5422 | 0.6734 | 0.2696 | 0.481 | 0.2097 | 0.4036 | 0.1463 | 0.3938 | 0.2861 | 0.4093 |
| 0.6394 | 90.0 | 9630 | 1.3503 | 0.2913 | 0.5775 | 0.2445 | 0.159 | 0.2181 | 0.4954 | 0.2935 | 0.4579 | 0.4713 | 0.2362 | 0.4156 | 0.6884 | 0.5438 | 0.6784 | 0.2697 | 0.4835 | 0.2041 | 0.4045 | 0.1519 | 0.3862 | 0.2869 | 0.404 |
| 0.6301 | 91.0 | 9737 | 1.3381 | 0.2914 | 0.5775 | 0.2397 | 0.1611 | 0.2246 | 0.4956 | 0.2963 | 0.4591 | 0.4737 | 0.241 | 0.426 | 0.6906 | 0.5477 | 0.6802 | 0.262 | 0.4772 | 0.2076 | 0.408 | 0.1554 | 0.4 | 0.2843 | 0.4031 |
| 0.632 | 92.0 | 9844 | 1.3426 | 0.2911 | 0.573 | 0.2416 | 0.1699 | 0.2282 | 0.4914 | 0.2991 | 0.4619 | 0.4751 | 0.2436 | 0.4282 | 0.6894 | 0.5428 | 0.6833 | 0.2679 | 0.4886 | 0.2091 | 0.3955 | 0.1519 | 0.4077 | 0.2836 | 0.4004 |
| 0.6231 | 93.0 | 9951 | 1.3458 | 0.294 | 0.5787 | 0.2491 | 0.1695 | 0.2263 | 0.4898 | 0.298 | 0.4621 | 0.4764 | 0.2407 | 0.4302 | 0.6841 | 0.546 | 0.6784 | 0.2697 | 0.4797 | 0.2138 | 0.404 | 0.1485 | 0.4062 | 0.292 | 0.4138 |
| 0.6162 | 94.0 | 10058 | 1.3339 | 0.2915 | 0.5819 | 0.2501 | 0.1638 | 0.2224 | 0.491 | 0.2978 | 0.4607 | 0.477 | 0.2425 | 0.4217 | 0.697 | 0.5445 | 0.6793 | 0.2713 | 0.4759 | 0.2092 | 0.404 | 0.1482 | 0.4215 | 0.2844 | 0.404 |
| 0.6249 | 95.0 | 10165 | 1.3501 | 0.2905 | 0.583 | 0.2494 | 0.1697 | 0.2233 | 0.4881 | 0.2969 | 0.4602 | 0.4746 | 0.243 | 0.4209 | 0.6892 | 0.5373 | 0.6739 | 0.2717 | 0.4937 | 0.2109 | 0.4022 | 0.1501 | 0.4046 | 0.2826 | 0.3987 |
| 0.6189 | 96.0 | 10272 | 1.3529 | 0.2904 | 0.5798 | 0.2422 | 0.1658 | 0.2242 | 0.4877 | 0.2938 | 0.4631 | 0.4772 | 0.2482 | 0.4268 | 0.6855 | 0.5395 | 0.6788 | 0.279 | 0.4924 | 0.2083 | 0.3973 | 0.1388 | 0.4154 | 0.2865 | 0.4022 |
| 0.6135 | 97.0 | 10379 | 1.3553 | 0.2929 | 0.5853 | 0.248 | 0.167 | 0.2251 | 0.4901 | 0.2972 | 0.4593 | 0.4765 | 0.2379 | 0.4222 | 0.6929 | 0.5369 | 0.6788 | 0.2785 | 0.4861 | 0.2083 | 0.3987 | 0.1524 | 0.4138 | 0.2887 | 0.4049 |
| 0.613 | 98.0 | 10486 | 1.3622 | 0.2938 | 0.5851 | 0.2432 | 0.1642 | 0.2285 | 0.4894 | 0.2958 | 0.4603 | 0.4747 | 0.2408 | 0.4212 | 0.6916 | 0.5406 | 0.6766 | 0.2774 | 0.4848 | 0.2088 | 0.3938 | 0.1525 | 0.4092 | 0.2899 | 0.4089 |
| 0.6144 | 99.0 | 10593 | 1.3536 | 0.2933 | 0.5833 | 0.245 | 0.1634 | 0.2282 | 0.4935 | 0.2962 | 0.4589 | 0.4745 | 0.2419 | 0.4225 | 0.6914 | 0.5414 | 0.6793 | 0.277 | 0.4873 | 0.2061 | 0.3942 | 0.1554 | 0.4092 | 0.2869 | 0.4027 |
| 0.613 | 100.0 | 10700 | 1.3550 | 0.2936 | 0.5835 | 0.2461 | 0.1635 | 0.2284 | 0.4956 | 0.2943 | 0.4587 | 0.4737 | 0.2424 | 0.4204 | 0.6907 | 0.5418 | 0.6788 | 0.2801 | 0.481 | 0.2075 | 0.3955 | 0.1534 | 0.4108 | 0.2852 | 0.4022 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
| {"license": "apache-2.0", "tags": ["object-detection", "vision", "generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro", "results": []}]} | qubvel-hf/facebook-detr-resnet-50-finetuned-10k-cppe5-manual-pad-repro | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"vision",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:14:24+00:00 |
null | null | {"license": "llama2"} | mnimonik/text_Reader | null | [
"license:llama2",
"region:us"
]
| null | 2024-04-25T19:15:01+00:00 |
|
question-answering | transformers | {} | lanzv/ClinicalBERTPRQABmbert_97_111_CS | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-25T19:15:19+00:00 |
|
null | null | {"license": "apache-2.0"} | HonzaS/routing | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-25T19:15:20+00:00 |
|
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [winninghealth/WiNGPT2-Llama-3-8B-Base](https://huggingface.co/winninghealth/WiNGPT2-Llama-3-8B-Base)
* [johnsnowlabs/JSL-MedLlama-3-8B-v1.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
layer_range: [0, 32]
- model: winninghealth/WiNGPT2-Llama-3-8B-Base
layer_range: [0, 32]
merge_method: slerp
base_model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["winninghealth/WiNGPT2-Llama-3-8B-Base", "johnsnowlabs/JSL-MedLlama-3-8B-v1.0"]} | arcee-ai/Llama-3-Medical-JSL-WiNGPT2-SLERP | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:winninghealth/WiNGPT2-Llama-3-8B-Base",
"base_model:johnsnowlabs/JSL-MedLlama-3-8B-v1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:15:24+00:00 |
null | fastai |
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
| {"tags": ["fastai"]} | PablitoGil14/ModelFuturama | null | [
"fastai",
"has_space",
"region:us"
]
| null | 2024-04-25T19:17:08+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** dbands
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | dbands/llama-3-8b-sql-instruct_4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-25T19:17:26+00:00 |
null | null | {} | Bevzonik/Samokat | null | [
"region:us"
]
| null | 2024-04-25T19:17:51+00:00 |
|
null | null | {} | MoTalaat/movie-falcon_V1 | null | [
"region:us"
]
| null | 2024-04-25T19:19:12+00:00 |
|
null | null | {} | ashishp-wiai/Rice_LoRA_100-2024-04-25 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-25T19:19:49+00:00 |
|
null | null | {} | nthakur/mistral-7b-instruct-v0.2-miracl-raft-sft-25th-apr-v1.0 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-25T19:20:01+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7956
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9486 | 1.0 | 1309 | 3.8083 |
| 3.8555 | 2.0 | 2618 | 3.7966 |
| 3.8179 | 3.0 | 3927 | 3.7956 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "distilbert/distilgpt2", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]} | JasssZ/my_awesome_eli5_clm-model | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-25T19:20:03+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
pygmalion-instruct - bnb 8bits
- Model creator: https://huggingface.co/alpindale/
- Original model: https://huggingface.co/alpindale/pygmalion-instruct/
Original model description:
---
license: mit
---
## Model Details
Experimental model. Trained with the [Pygmalion](https://huggingface.co/PygmalionAI/pygmalion-6b/tree/dev) and the [WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored) datasets.
The purpose of this model is to enable complex Instruct prompting but with the RP capabilties of Pygmalion.
### Prompting format
```
instruction:
output:
```
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
### Uses
The intended use-case is Role-Playing with Instruct prompts. Guiding the bot towards a certain conversation style should be easier this way. Subject to experimentation.
### Out-of-Scope Use
- Assistant Bot [subject to providing incorrect instructions]
- Complex multi-character chat
### Risks
The model can generate potentially harmful or NSFW outputs. Please use with caution.
### Citation
WizardLM:
```
@misc{xu2023wizardlm,
title={WizardLM: Empowering Large Language Models to Follow Complex Instructions},
author={Can Xu and Qingfeng Sun and Kai Zheng and Xiubo Geng and Pu Zhao and Jiazhan Feng and Chongyang Tao and Daxin Jiang},
year={2023},
eprint={2304.12244},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| {} | RichardErkhov/alpindale_-_pygmalion-instruct-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:2304.12244",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-25T19:20:10+00:00 |
null | null | {} | vishal0719/DNN_Upscaler | null | [
"region:us"
]
| null | 2024-04-25T19:20:32+00:00 |
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