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|---|---|---|---|---|---|---|---|---|
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 π€ 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
EpicJhon/llama_255
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T22:38:46+00:00
|
null |
transformers
|
# jeiku/Average_Normie_l3_v1_8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`jeiku/Average_Normie_l3_v1_8B`](https://huggingface.co/jeiku/Average_Normie_l3_v1_8B) 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/jeiku/Average_Normie_l3_v1_8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo jeiku/Average_Normie_l3_v1_8B-Q4_K_M-GGUF --model average_normie_l3_v1_8b.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo jeiku/Average_Normie_l3_v1_8B-Q4_K_M-GGUF --model average_normie_l3_v1_8b.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m average_normie_l3_v1_8b.Q4_K_M.gguf -n 128
```
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["grimulkan/theory-of-mind"], "base_model": ["jeiku/Average_Normie_l3_v0_8B", "ResplendentAI/Theory_of_Mind_Llama3"]}
|
jeiku/Average_Normie_l3_v1_8B-Q4_K_M-GGUF
| null |
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:grimulkan/theory-of-mind",
"base_model:jeiku/Average_Normie_l3_v0_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T22:39:54+00:00
|
text-generation
|
transformers
|
# Llama 3 8B Instruct β OmniQuant
Based on [Llama 3 8B Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct).
Quantized with [OmniQuant](https://github.com/OpenGVLab/OmniQuant).
## Evaluation
### PPL (β)
| | wiki |
| --------- | ---- |
| FP | 8,29 |
| **Quantized** | 8,97 |
### Accuracy on English Benchmarks, % (β)
| | piqa | arc_easy | arc_challenge | boolq | hellaswag | winogrande | mmlu_humanities | mmlu_social_sciences | mmlu_stem | mmlu_other |
| --------- | ---- | -------- | ------------- | ----- | --------- | ---------- | --------------- | -------------------- | --------- | ---------- |
| FP | 78,7 | 81,6 | 53,0 | 83,1 | 57,7 | 72,1 | 67,0 | 70,9 | 54,5 | 68,2 |
| **Quantized** | 77,2 | 80,7 | 51,8 | 82,8 | 56,8 | 72,5 | 63,4 | 67,6 | 50,1 | 65,0 |
### Accuracy on Russian Benchmarks, % (β)
| | danetqa | terra | rwsd | muserc | rucos | lidirus | parus | rcb | russe | rucola |
| --------- | ------- | ----- | ---- | ------ | ----- | ------- | ----- | ---- | ----- | ------ |
| FP | 78,6 | 60,9 | 65,7 | 56,1 | 64,9 | 63,2 | 71,0 | 34,1 | 60,8 | 64,1 |
| **Quantized** | 71,6 | 60,6 | 52,5 | 63,7 | 57,3 | 57,2 | 74,0 | 33,6 | 36,9 | 67,5 |
### Summary
| | Avg acc diff on Eng, % (β) | Avg acc diff on Rus, % (β) | Occupied disk space, % (β) |
| --------- | -------------------------- | -------------------------- | -------------------------- |
| FP | 0 | 0 | 100 |
| **Quantized** | \-1,9 | \-4,5 | 35,7 |
## Examples
### Imports and Model Loading
<details>
<summary>Expand</summary>
```python
import gc
import auto_gptq.nn_modules.qlinear.qlinear_cuda as qlinear_cuda
import auto_gptq.nn_modules.qlinear.qlinear_triton as qlinear_triton
import torch
from accelerate import (
init_empty_weights,
infer_auto_device_map,
load_checkpoint_in_model,
)
from tqdm import tqdm
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
pipeline,
)
def get_named_linears(model):
return {
name: module for name, module in model.named_modules()
if isinstance(module, torch.nn.Linear)
}
def set_module(model, name, module):
parent = model
levels = name.split('.')
for i in range(len(levels) - 1):
cur_name = levels[i]
if cur_name.isdigit():
parent = parent[int(cur_name)]
else:
parent = getattr(parent, cur_name)
setattr(parent, levels[-1], module)
def load_model(model_path):
# Based on: https://github.com/OpenGVLab/OmniQuant/blob/main/runing_quantized_mixtral_7bx8.ipynb
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
if not hasattr(config, 'quantization_config'):
raise AttributeError(
f'No quantization info found in model config "{model_path}"'
f' (`quantization_config` section is missing).'
)
wbits = config.quantization_config['bits']
group_size = config.quantization_config['group_size']
# We are going to init an ordinary model and then manually replace all Linears with QuantLinears
del config.quantization_config
with init_empty_weights():
model = AutoModelForCausalLM.from_config(config=config, torch_dtype=torch.float16, trust_remote_code=True)
layers = model.model.layers
for i in tqdm(range(len(layers))):
layer = layers[i]
named_linears = get_named_linears(layer)
for name, module in named_linears.items():
params = (
wbits, group_size,
module.in_features, module.out_features,
module.bias is not None
)
if wbits in [2, 4]:
q_linear = qlinear_triton.QuantLinear(*params)
elif wbits == 3:
q_linear = qlinear_cuda.QuantLinear(*params)
else:
raise NotImplementedError("Only 2, 3 and 4 bits are supported.")
q_linear.to(next(layer.parameters()).device)
set_module(layer, name, q_linear)
torch.cuda.empty_cache()
gc.collect()
model.tie_weights()
device_map = infer_auto_device_map(model)
print("Loading pre-computed quantized weights...")
load_checkpoint_in_model(
model, checkpoint=model_path,
device_map=device_map, offload_state_dict=True,
)
print("Model loaded successfully!")
return model
```
</details>
### Inference
```python
model_path = "compressa-ai/Llama-3-8B-Instruct-OmniQuant"
model = load_model(model_path).cuda()
tokenizer = AutoTokenizer.from_pretrained(
model_path, use_fast=False, trust_remote_code=True
)
# Llama 3 "specifics"
# https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/discussions/4
terminators = [
tokenizer.convert_tokens_to_ids("<|end_of_text|>"),
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
system_message = "You are a friendly chatbot who always responds in the style of a pirate."
user_message = "Where are we going, Captain?"
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": user_message},
]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt")
inputs = {k: v.cuda() for k, v in inputs.items()}
outputs = model.generate(
**inputs, max_new_tokens=512,
do_sample=True, temperature=0.7, top_p=0.95,
eos_token_id=terminators,
)
response = tokenizer.decode(outputs[0])
continuation = response.removeprefix(prompt).removesuffix(tokenizer.eos_token)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
```
### Inference Using Pipeline
```python
pipe = pipeline(
"text-generation",
model=model, tokenizer=tokenizer,
eos_token_id=terminators,
max_new_tokens=512, do_sample=True,
temperature=0.7, top_p=0.95,
device=0,
)
prompt = pipe.tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
outputs = pipe(prompt)
response = outputs[0]["generated_text"]
continuation = response.removeprefix(prompt)
print(f'Prompt:\n{prompt}')
print(f'Continuation:\n{continuation}\n')
```
|
{"license": "other", "tags": ["llama3", "omniquant", "gptq", "triton"], "base_model": "NousResearch/Meta-Llama-3-8B-Instruct", "model_type": "llama", "pipeline_tag": "text-generation", "quantized_by": "Compressa", "license_name": "llama3", "license_link": "https://llama.meta.com/llama3/license"}
|
compressa-ai/Llama-3-8B-Instruct-OmniQuant
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama3",
"omniquant",
"gptq",
"triton",
"conversational",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-23T22:40:25+00:00
|
question-answering
|
transformers
|
{}
|
titanbot/Roberta-Large-SQUADV2
| null |
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T22:43:02+00:00
|
|
text-generation
|
transformers
|
# llama-3-slerp-kraut-dragon-8B
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:
* [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
* [nbeerbower/llama-3-dragonmaid-8B](https://huggingface.co/nbeerbower/llama-3-dragonmaid-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
layer_range: [0, 32]
- model: nbeerbower/llama-3-dragonmaid-8B
layer_range: [0, 32]
merge_method: slerp
base_model: nbeerbower/llama-3-dragonmaid-8B
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
```
|
{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct", "nbeerbower/llama-3-dragonmaid-8B"], "license_name": "llama3"}
|
nbeerbower/llama-3-slerp-kraut-dragon-8B
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct",
"base_model:nbeerbower/llama-3-dragonmaid-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T22:46:13+00:00
|
text-generation
|
transformers
|
**I messed up on this model** and forgot to add special tokens for <|im_start|> & <|im_end|> - but it still seems to work fine. If you're having any issues let me know in the Discussions tab and I can retrain it.
<div style="background-color: rgb(255, 241, 209); color: rgb(90, 65, 0); border-radius: 8px; padding: 0.5rem 1rem; ">
<b>This model is usable, but you can get better results with the fixed latest model!</b> Check out the <a href="https://huggingface.co/mrfakename/refusal" style="color: rgb(90, 65, 0);">latest model</a> instead of using this one!
</div>
---
A tiny 1B model that refuses basically anything you ask it! Trained on the [refusal](https://huggingface.co/datasets/mrfakename/refusal) dataset. Prompt format is ChatML.
Training results:
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4352 | 0.0580 | 1 | 2.4462 |
| 1.5742 | 0.5217 | 9 | 1.4303 |
| 1.5084 | 1.0435 | 18 | 1.3672 |
| 1.0814 | 1.5217 | 27 | 1.3483 |
| 1.1024 | 2.0435 | 36 | 1.3204 |
| 0.6554 | 2.5217 | 45 | 1.4286 |
| 0.6163 | 3.0435 | 54 | 1.4375 |
| 0.5058 | 3.5072 | 63 | 1.4908 |
Training hyperparemeters:
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Base model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
|
{"language": ["en"], "library_name": "transformers", "datasets": ["mrfakename/refusal"], "pipeline_tag": "text-generation", "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"}
|
mrfakename/refusal-old
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:mrfakename/refusal",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T22:46:41+00:00
|
null | null |
{"license": "openrail"}
|
GoldoBasic/barrybuns2
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-23T22:47:26+00:00
|
|
null | null |
{"license": "unlicense"}
|
Cronos26/Crons
| null |
[
"license:unlicense",
"region:us"
] | null |
2024-04-23T22:48:19+00:00
|
|
null | null |
An attempt to make ParasiticRogue's model a tad better on longer contexts. I just ran the script, all credit for the original merge goes to my friend.
Exl2 quants already being uploaded by him:
https://huggingface.co/ParasiticRogue/RP-Stew-v2.5-34B-exl2-4.65
Also, my samplers, instruct and prompt for the model (works wonderfully):
Samplers: https://files.catbox.moe/1yhmve.json
Instruct: https://files.catbox.moe/mp4s7h.json
Story String: https://files.catbox.moe/98xlsb.json
```
models:
- model: F:\Merge\ParasiticRogue_Nontoxic-PiVoT-Bagel-RP-34b
parameters:
weight: 0.16
density: 0.42
- model: F:\Merge\ParasiticRogue_Nyakura-CausalLM-RP-34B
parameters:
weight: 0.22
density: 0.54
- model: F:\Merge\migtissera_Tess-34B-v1.5b
parameters:
weight: 0.28
density: 0.66
- model: F:\Merge\brucethemoose_Capybara-Fixed-Temp
parameters:
weight: 0.34
density: 0.78
merge_method: dare_ties
base_model: F:\Merge\chargoddard_Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
|
{}
|
MarinaraSpaghetti/RP-Stew-v2.5-34B
| null |
[
"safetensors",
"region:us"
] | null |
2024-04-23T22:50:35+00:00
|
token-classification
|
transformers
|
{}
|
titanbot/Roberta-Large-CONLL2003
| null |
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T22:51:39+00:00
|
|
text-generation
|
transformers
|
{"license": "apache-2.0"}
|
cemt/WikiSQL-Phi-2-Super
| null |
[
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T22:52:14+00:00
|
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# image-captioning-output
This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5164
- Rouge1: 35.5267
- Rouge2: 12.254
- Rougel: 32.968
- Rougelsum: 32.9723
- Gen Len: 12.395
## 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
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.5193 | 0.25 | 500 | 0.5171 | 33.0319 | 10.364 | 30.6939 | 30.6888 | 12.1 |
| 0.4842 | 0.5 | 1000 | 0.5102 | 33.7318 | 10.8199 | 31.1842 | 31.18 | 11.3 |
| 0.4724 | 0.75 | 1500 | 0.5028 | 34.6981 | 11.4074 | 31.9128 | 31.9158 | 12.02 |
| 0.4632 | 1.0 | 2000 | 0.5012 | 35.9443 | 12.8742 | 33.4061 | 33.377 | 11.04 |
| 0.377 | 1.25 | 2500 | 0.5026 | 35.7745 | 12.2309 | 33.3234 | 33.3353 | 11.735 |
| 0.3819 | 1.5 | 3000 | 0.5018 | 36.0145 | 13.0296 | 33.5985 | 33.6182 | 12.285 |
| 0.3788 | 1.75 | 3500 | 0.5030 | 35.9016 | 12.5276 | 33.4995 | 33.5033 | 11.305 |
| 0.3654 | 2.0 | 4000 | 0.5020 | 36.2476 | 12.945 | 33.6453 | 33.6595 | 11.9 |
| 0.3102 | 2.25 | 4500 | 0.5146 | 36.1507 | 13.0072 | 33.3889 | 33.3786 | 12.305 |
| 0.3137 | 2.5 | 5000 | 0.5166 | 35.7413 | 12.5693 | 33.2646 | 33.2508 | 12.71 |
| 0.3111 | 2.75 | 5500 | 0.5171 | 35.5658 | 12.511 | 33.0581 | 33.0518 | 12.55 |
| 0.3023 | 3.0 | 6000 | 0.5164 | 35.5267 | 12.254 | 32.968 | 32.9723 | 12.395 |
### Framework versions
- Transformers 4.40.0
- 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": "nlpconnect/vit-gpt2-image-captioning", "model-index": [{"name": "image-captioning-output", "results": []}]}
|
NourFakih/image-captioning-output
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"base_model:nlpconnect/vit-gpt2-image-captioning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T22:52:24+00:00
|
null | null |
{}
|
Nilsonbar/Iptv
| null |
[
"region:us"
] | null |
2024-04-23T22:52:28+00:00
|
|
null | null |
{}
|
dasfdsewfdsf/gloraxl
| null |
[
"region:us"
] | null |
2024-04-23T22:53:49+00:00
|
|
text-classification
|
transformers
|
{}
|
greasyFinger/german_xl
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T22:56:43+00:00
|
|
null | null |
{"license": "mit"}
|
pjh64/Phi-3-mini-128K-Instruct.gguf
| null |
[
"gguf",
"license:mit",
"region:us"
] | null |
2024-04-23T22:58:54+00:00
|
|
text-classification
|
transformers
|
# BERT-based Organization Detection Model for Turkish Texts
## Model Description
This model is fine-tuned on the `dbmdz/bert-base-turkish-uncased` architecture for detecting organization accounts within Turkish Twitter. This initiative is part of the Politus Project's efforts to analyze organizational presence in social media data.
## Model Architecture
- **Base Model:** BERT (dbmdz/bert-base-turkish-uncased)
- **Training Data:** Twitter data from 4,000 random accounts and 4,000 accounts with high organization-related activity as determined by m3inference scores above 0.7, 8,000 accounts in total. The data was annotated based on user names, screen names, and descriptions using ChatGPT 4.
## Training Setup
- **Tokenization:** Used Hugging Face's AutoTokenizer, padding sequences to a maximum length of 128 tokens.
- **Dataset Split:** 80% training, 20% validation.
- **Training Parameters:**
- Epochs: 3
- Training batch size: 8
- Evaluation batch size: 16
- Warmup steps: 500
- Weight decay: 0.01
## Hyperparameter Tuning
Performed using Optuna, achieving best settings:
- **Learning rate:** 1.84e-05
- **Batch size:** 16
- **Epochs:** 3
## Evaluation Metrics
- **Precision on Validation Set:** 0.67 (organization class)
- **Recall on Validation Set:** 0.81 (organization class)
- **F1-Score (Macro Average):** 0.73
- **Accuracy:** 0.94
- **Confusion Matrix on Validation Set:**
```
[[1390, 60],
[ 28, 122]]
```
- **Hand-coded Sample of 100 Accounts:**
- **Precision:** 0.89
- **Recall:** 0.89
- **F1-Score (Macro Average):** 0.89
- **Confusion Matrix:**
```
[[935, 4],
[ 4, 31]]
```
## How to Use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("atsizelti/turkish_org_classifier")
tokenizer = AutoTokenizer.from_pretrained("atsizelti/turkish_org_classifier")
text = "Γrnek metin buraya girilir."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
```
|
{"language": "tr", "license": "apache-2.0", "tags": ["bert", "turkish", "text-classification"], "datasets": ["custom"], "metrics": ["precision", "recall", "f1", "accuracy"]}
|
atsizelti/turkish_org_classifier
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"turkish",
"tr",
"dataset:custom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:00:16+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. -->
# 0.01_ablation_5iters_bs256_nodpo_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.01_ablation_5iters_bs256_nodpo_iter_1", "results": []}]}
|
ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:01:55+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. -->
# 0.001_ablation_5iters_bs256_nodpo_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_ablation_5iters_bs256_nodpo_iter_1", "results": []}]}
|
ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:01:56+00:00
|
null | null |
{}
|
dasfdsewfdsf/lloraxl
| null |
[
"region:us"
] | null |
2024-04-23T23:02:57+00:00
|
|
reinforcement-learning
| null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="cmattoon/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
|
cmattoon/q-FrozenLake-v1-4x4-noSlippery
| null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null |
2024-04-23T23:03:54+00:00
|
null | null |
{}
|
ixw/sd21-celeba-lora
| null |
[
"region:us"
] | null |
2024-04-23T23:05:00+00:00
|
|
text-generation
|
transformers
|
# jeiku/Average_Normie_l3_v0_8B AWQ
- Model creator: [jeiku](https://huggingface.co/jeiku)
- Original model: [Average_Normie_l3_v0_8B](https://huggingface.co/jeiku/Average_Normie_l3_v0_8B)
## 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/Average_Normie_l3_v0_8B-AWQ"
system_message = "You are Average_Normie_l3_v0_8B, incarnated as a powerful AI. You were created by jeiku."
# 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
|
{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}
|
solidrust/Average_Normie_l3_v0_8B-AWQ
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:06:05+00:00
|
null | null |
{"license": "mit"}
|
Ritual-Net/california-housing
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-23T23:07:25+00:00
|
|
null | null |
{}
|
markpotanin/llama2_confinqa
| null |
[
"region:us"
] | null |
2024-04-23T23:07:57+00:00
|
|
reinforcement-learning
| null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="cmattoon/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}
|
cmattoon/Taxi-v3
| null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null |
2024-04-23T23:08:01+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. -->
# wav2vec2_large_1.1
This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1063
- Wer: 1.0
- Cer: 0.9997
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 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: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:|
| 3.8007 | 1.12 | 700 | 3.0604 | 1.0 | 0.9997 |
| 3.3783 | 2.24 | 1400 | 3.2217 | 1.0 | 0.9997 |
| 3.2938 | 3.36 | 2100 | 3.1471 | 1.0 | 0.9997 |
| 3.2049 | 4.48 | 2800 | 3.1063 | 1.0 | 0.9997 |
### 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"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-large", "model-index": [{"name": "wav2vec2_large_1.1", "results": []}]}
|
Myriam123/wav2vec2_large_1.1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:09:04+00:00
|
null | null |
{}
|
duck2143/test
| null |
[
"region:us"
] | null |
2024-04-23T23:11:10+00:00
|
|
image-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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3172
- Accuracy: 0.9193
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.1594 | 0.9950 | 99 | 0.8767 | 0.7523 |
| 0.536 | 2.0 | 199 | 0.3970 | 0.8974 |
| 0.3935 | 2.9849 | 297 | 0.3172 | 0.9193 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/convnext-tiny-224", "model-index": [{"name": "convnext-tiny-224-finetuned-eurosat-albumentations", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9193205944798302, "name": "Accuracy"}]}]}]}
|
samayl24/convnext-tiny-224-finetuned-eurosat-albumentations
| null |
[
"transformers",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/convnext-tiny-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:13:23+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.20 +/- 0.07", "name": "mean_reward", "verified": false}]}]}]}
|
ahforoughi/a2c-PandaReachDense-v3
| null |
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-23T23:15:32+00:00
|
null |
diffusers
|
{}
|
tianyi0216/model5
| null |
[
"diffusers",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | null |
2024-04-23T23:18:17+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 π€ 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": []}
|
juewang/Meta-Llama-3-8B-wo-gqa
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:19:54+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. -->
[<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: beomi/Llama-3-Open-Ko-8B-Instruct-preview
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: sosoai/mixed_dataset
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_sample_packing: False
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# out
This model is a fine-tuned version of [beomi/Llama-3-Open-Ko-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-Open-Ko-8B-Instruct-preview) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9380
## 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: 1
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9558 | 0.06 | 1 | 2.0462 |
| 1.8918 | 0.52 | 9 | 1.9380 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
{"license": "other", "tags": ["generated_from_trainer"], "base_model": "beomi/Llama-3-Open-Ko-8B-Instruct-preview", "model-index": [{"name": "out", "results": []}]}
|
sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-pytorch-v0.1
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:beomi/Llama-3-Open-Ko-8B-Instruct-preview",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:20:55+00:00
|
null | null |
{}
|
justin-shopcapsule/ddpm-belts-512
| null |
[
"region:us"
] | null |
2024-04-23T23:22:11+00:00
|
|
null | null |
{"license": "openrail"}
|
AmanBOT/Ted
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-23T23:24:40+00:00
|
|
token-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. -->
# distilbert-base-uncased_finetuned_ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0013
- Precision: 0.9420
- Recall: 0.9553
- F1: 0.9486
- Accuracy: 0.9997
## 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: 20
- eval_batch_size: 20
- 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 341 | 0.0032 | 0.8657 | 0.8682 | 0.8670 | 0.9993 |
| 0.0338 | 2.0 | 682 | 0.0017 | 0.9366 | 0.9372 | 0.9369 | 0.9997 |
| 0.0021 | 3.0 | 1023 | 0.0013 | 0.9420 | 0.9553 | 0.9486 | 0.9997 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased_finetuned_ner", "results": []}]}
|
Cerastes/distilbert-base-uncased_finetuned_ner
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:25:08+00:00
|
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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": []}
|
dayoon/e5_new_loss_epoch1
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:27:25+00:00
|
text-to-image
| null |
# Hyper-SD - Onnx Olive DirectML Optimized
## Original Model
https://huggingface.co/ByteDance/Hyper-SD
## C# Inference Demo
https://github.com/saddam213/OnnxStack
```csharp
// Create Pipeline
var pipeline = StableDiffusionXLPipeline.CreatePipeline("D:\\Models\\Hyper-SD-onnx");
// Prompt
var promptOptions = new PromptOptions
{
Prompt = "photo of a cat"
};
// Scheduler Options
var schedulerOptions = pipeline.DefaultSchedulerOptions with
{
InferenceSteps = 1,
GuidanceScale = 0,
SchedulerType = SchedulerType.DDIM,
Timesteps = new List<int> { 800 }
};
// Run pipeline
var result = await pipeline.GenerateImageAsync(promptOptions, schedulerOptions);
// Save Image Result
await result.SaveAsync("Result.png");
```
## Inference Result
|
{"pipeline_tag": "text-to-image"}
|
saddam213/Hyper-SD-onnx
| null |
[
"onnx",
"text-to-image",
"region:us"
] | null |
2024-04-23T23:27:52+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 [andreasmadsen/efficient_mlm_m0.40](https://huggingface.co/andreasmadsen/efficient_mlm_m0.40) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0610
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1265 | 1.0 | 1332 | 0.0689 |
| 0.0717 | 2.0 | 2664 | 0.0622 |
| 0.0663 | 3.0 | 3996 | 0.0610 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "andreasmadsen/efficient_mlm_m0.40", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]}
|
CornCube/my_awesome_eli5_clm-model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta-prelayernorm",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:andreasmadsen/efficient_mlm_m0.40",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:28:26+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** 1024m
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
- **Part of EXALT shared task submission : fine-tuned on train data - v001**
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"}
|
1024m/LLAMA3-01-EXALT-1A-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 |
2024-04-23T23:28:33+00:00
|
null | null |
{}
|
Nathan757/lanovoice1-tts-149
| null |
[
"region:us"
] | null |
2024-04-23T23:28:43+00:00
|
|
text-to-image
|
diffusers
|
# 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 𧨠diffusers 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": "diffusers"}
|
rubbrband/yacam_SR36476
| null |
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-23T23:28:43+00:00
|
text-generation
|
transformers
|
{}
|
jaredjoss/pythia-410m-roberta-lr_8e7-kl_01-steps_12000-rlhf-model
| null |
[
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:30:35+00:00
|
|
null | null |
{"license": "apache-2.0"}
|
maddycodes/mistral-7b-tuned
| null |
[
"safetensors",
"license:apache-2.0",
"region:us"
] | null |
2024-04-23T23:30:50+00:00
|
|
null | null |
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
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[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
## This repo contains GGUF versions of the microsoft/Phi-3-mini-4k-instruct model.
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with GGUF.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***What is the model format?*** We use GGUF format.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
# Downloading and running the models
You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/):
| Quant type | Description |
|------------|--------------------------------------------------------------------------------------------|
| Q5_K_M | High quality, recommended. |
| Q5_K_S | High quality, recommended. |
| Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. |
| Q4_K_S | Slightly lower quality with more space savings, recommended. |
| IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. |
| IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Q3_K_L | Lower quality but usable, good for low RAM availability. |
| Q3_K_M | Even lower quality. |
| IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| Q3_K_S | Low quality, not recommended. |
| IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| Q2_K | Very low quality but surprisingly usable. |
## How to download GGUF files ?
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
- **Option A** - Downloading in `text-generation-webui`:
- **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/Phi-3-mini-4k-instruct-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf.
- **Step 2**: Then click Download.
- **Option B** - Downloading on the command line (including multiple files at once):
- **Step 1**: We recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
- **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download PrunaAI/Phi-3-mini-4k-instruct-GGUF-smashed Phi-3-mini-4k-instruct.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
Alternatively, you can also download multiple files at once with a pattern:
```shell
huggingface-cli download PrunaAI/Phi-3-mini-4k-instruct-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/Phi-3-mini-4k-instruct-GGUF-smashed Phi-3-mini-4k-instruct.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## How to run model in GGUF format?
- **Option A** - Introductory example with `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Phi-3-mini-4k-instruct.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
- **Option B** - Running in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 β Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).
- **Option C** - Running from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Phi-3-mini-4k-instruct.IQ3_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Phi-3-mini-4k-instruct.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
- **Option D** - Running with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}
|
PrunaAI/Phi-3-mini-4k-instruct-GGUF-Imatrix-smashed
| null |
[
"gguf",
"pruna-ai",
"region:us"
] | null |
2024-04-23T23:31:12+00:00
|
null | null |
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.com/invite/vb6SmA3hxu)
## This repo contains GGUF versions of the microsoft/Phi-3-mini-128k-instruct model.
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help.
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with GGUF.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***What is the model format?*** We use GGUF format.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
# Downloading and running the models
You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/):
| Quant type | Description |
|------------|--------------------------------------------------------------------------------------------|
| Q5_K_M | High quality, recommended. |
| Q5_K_S | High quality, recommended. |
| Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. |
| Q4_K_S | Slightly lower quality with more space savings, recommended. |
| IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. |
| IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Q3_K_L | Lower quality but usable, good for low RAM availability. |
| Q3_K_M | Even lower quality. |
| IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| Q3_K_S | Low quality, not recommended. |
| IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| Q2_K | Very low quality but surprisingly usable. |
## How to download GGUF files ?
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
- **Option A** - Downloading in `text-generation-webui`:
- **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf.
- **Step 2**: Then click Download.
- **Option B** - Downloading on the command line (including multiple files at once):
- **Step 1**: We recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
- **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed Phi-3-mini-128k-instruct.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
Alternatively, you can also download multiple files at once with a pattern:
```shell
huggingface-cli download PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed Phi-3-mini-128k-instruct.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## How to run model in GGUF format?
- **Option A** - Introductory example with `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Phi-3-mini-128k-instruct.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
- **Option B** - Running in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 β Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp).
- **Option C** - Running from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Phi-3-mini-128k-instruct.IQ3_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<s>[INST] {prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Phi-3-mini-128k-instruct.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
- **Option D** - Running with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}
|
PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed
| null |
[
"gguf",
"pruna-ai",
"region:us"
] | null |
2024-04-23T23:31:13+00:00
|
reinforcement-learning
|
transformers
|
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="PranavBP525//tmp/tmp44w1s9gw/PranavBP525/phi-2-storygen-rlhf")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("PranavBP525//tmp/tmp44w1s9gw/PranavBP525/phi-2-storygen-rlhf")
model = AutoModelForCausalLMWithValueHead.from_pretrained("PranavBP525//tmp/tmp44w1s9gw/PranavBP525/phi-2-storygen-rlhf")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
{"license": "apache-2.0", "tags": ["trl", "ppo", "transformers", "reinforcement-learning"]}
|
PranavBP525/phi-2-storygen-rlhf
| null |
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:31:37+00:00
|
image-classification
|
transformers
|
{}
|
DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-10000
| null |
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:31:49+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_gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5744
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.6919 | 1.0 | 1333 | 3.5809 |
| 3.5769 | 2.0 | 2666 | 3.5744 |
| 3.5275 | 3.0 | 3999 | 3.5744 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_eli5_clm-model_gpt2", "results": []}]}
|
mkim-MASI/my_awesome_eli5_clm-model_gpt2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:31:55+00:00
|
text-generation
|
transformers
|
# β¨ Falcon-7B-Instruct
**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
*Paper coming soon π.*
π€ To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
* **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
π¬ **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
π₯ **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
π₯ **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct.
# Model Card for Falcon-7B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
| **Data source** | **Fraction** | **Tokens** | **Description** |
|--------------------|--------------|------------|-----------------------------------|
| [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat |
| [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct |
| [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct |
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
Note that this model variant is not optimized for NLP benchmarks.
## Technical Specifications
For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* π. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the π [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B-Instruct is made available under the Apache 2.0 license.
## Contact
[email protected]
|
{"language": ["en"], "license": "apache-2.0", "datasets": ["tiiuae/falcon-refinedweb"], "inference": true, "widget": [{"text": "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?", "example_title": "Abu Dhabi Trip"}, {"text": "What's the Everett interpretation of quantum mechanics?", "example_title": "Q/A: Quantum & Answers"}, {"text": "Give me a list of the top 10 dive sites you would recommend around the world.", "example_title": "Diving Top 10"}, {"text": "Can you tell me more about deep-water soloing?", "example_title": "Extreme sports"}, {"text": "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?", "example_title": "Twitter Helper"}, {"text": "What are the responsabilities of a Chief Llama Officer?", "example_title": "Trendy Jobs"}]}
|
titanbot/ct2-int8-falcon-7b-instruct
| null |
[
"transformers",
"RefinedWebModel",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:32:36+00:00
|
text-generation
|
transformers
|
# nebchi/kor-resume-Orion-14B-Q4_K_M-GGUF
This model was converted to GGUF format from [`nebchi/kor-resume-Orion-14B`](https://huggingface.co/nebchi/kor-resume-Orion-14B) 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/nebchi/kor-resume-Orion-14B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo nebchi/kor-resume-Orion-14B-Q4_K_M-GGUF --model kor-resume-orion-14b.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo nebchi/kor-resume-Orion-14B-Q4_K_M-GGUF --model kor-resume-orion-14b.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m kor-resume-orion-14b.Q4_K_M.gguf -n 128
```
|
{"language": ["ko"], "license": "other", "library_name": "transformers", "tags": ["pytorch", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"}
|
nebchi/kor-resume-Orion-14B-Q4_K_M-GGUF
| null |
[
"transformers",
"gguf",
"pytorch",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"ko",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:33:03+00:00
|
text-generation
|
transformers
|
# lodrick-the-lafted/Copus-2x8B AWQ
- Model creator: [lodrick-the-lafted](https://huggingface.co/lodrick-the-lafted)
- Original model: [Copus-2x8B](https://huggingface.co/lodrick-the-lafted/Copus-2x8B)
<img src=https://huggingface.co/lodrick-the-lafted/Copus-2x8B/resolve/main/copus.png>
## Model Summary
MoE'd up:
- [dreamgen/opus-v1.2-llama-3-8b](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b)
- [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)_
## 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/Copus-2x8B-AWQ"
system_message = "You are Copus-2x8B, incarnated as a powerful AI. You were created by lodrick-the-lafted."
# 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
|
{"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}
|
solidrust/Copus-2x8B-AWQ
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:33:13+00:00
|
text-generation
|
transformers
|
#### Do not run the inference from Model card it won't work!!
#### Ignore config.json
#### Use this model space instead!!
https://huggingface.co/spaces/Chrisneverdie/SportsRWKV
This model is built on RWKV 6 structure - an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
This is also part of a project to explore the potential of the Small Language Model in the Sports domain.
Our previous projects:
https://github.com/chrischenhub/FirstSportsELM
https://huggingface.co/spaces/Chrisneverdie/SportsDPT
This model is finetuned with QA pairs so a text completion task may result in an error.
Questions unrelated to sports may suffer from poor performance.
It may still provide incorrect information so just take it as a toy domain model.
# SportsRWKV
Created by Chris Zexin Chen
Email for question: [email protected]
As avid sports enthusiasts, weβve consistently observed a gap in the market for a dedicated
large language model tailored to the sports domain. This research stems from our intrigue
about the potential of a language model that is exclusively trained and fine-tuned on sports-
related data. We aim to assess its performance against generic language models, thus delving
into the unique nuances and demands of the sports industry
This model structure is built by BlinkDL: https://github.com/BlinkDL/RWKV-LM
### Pretrain Data
https://huggingface.co/datasets/Chrisneverdie/SportsRWKV
*fixed_text_document.bin&fixed_text_document.idx ~8.4 Gb/4.5B tokens*
## Pretrain
To replicate our model, you need to use fixed_text_document.bin & fixed_text_document.idx, which is processed and ready to train.
We trained on a 2xH100 80GB node for 5 hrs to get a val loss ~2.305. Once you set up the environment:
For best performance, use python 3.10, torch 2.1.2+cu121 (or latest), cuda 12.3+, latest deepspeed, but keep pytorch-lightning==1.9.5
best performance:
```
pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu121
pip install pytorch-lightning==1.9.5 deepspeed wandb ninja --upgrade
```
First create the model:
```
python train.py --wandb "" --proj_dir "output/"\
--data_file "data/fixed_text_document" --data_type "binidx" --vocab_size 65536 --my_testing "x060"\
--ctx_len 1024 --my_pile_stage 1 --epoch_count 1 --epoch_begin 0 \
--epoch_save 1 --weight_decay 0 --head_size_a 64 \
--num_nodes 1 --micro_bsz 1 --n_layer 12 --n_embd 768 --pre_ffn 0 --head_qk 0 --my_exit_tokens 4534166811 --magic_prime 4427879 \
--lr_init 1e-5 --lr_final 1e-5 --warmup_steps 10 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 --my_pile_edecay 0 --accelerator cpu --devices 1 --precision bf16 --strategy deepspeed_stage_2 --grad_cp 0
```
Start training:
```
python train.py --load_model "0" --wandb "Test" --proj_dir "output/"
--my_testing "x060" --ctx_len 1024 --my_pile_stage 3 --epoch_count 999999 --epoch_begin 0 --data_file "data/fixed_text_document" --my_exit_tokens 4534166811 --magic_prime 4427879 \
--num_nodes 1 --micro_bsz 12 --n_layer 12 --n_embd 768 --pre_ffn 0 --head_qk 0 --lr_init 6e-4 --lr_final 6e-5 --warmup_steps 10 --beta1 0.9 --beta2 0.99 \
--adam_eps 1e-8 --my_pile_edecay 0 --data_type "binidx" --vocab_size 65536 --weight_decay 0.1 --epoch_save 5 --head_size_a 64 --accelerator gpu --devices 1 \
--precision bf16 --strategy deepspeed_stage_2 --grad_cp 0 --enable_progress_bar True --ds_bucket_mb 200
```
Note: if you are using commercial GPUs, change --grad_cp to 1 and --ds_bucket_mb to 2. Change --devices/--num_nodes if you have multiple GPUs/nodes
The loss should look like this
After you finish the training, the final .pth file will be saved under the output folder
## Fine Tune
We used thousands of GPT4-generated Sports QA pairs to finetune our model - specifics can be found under: https://github.com/chrischenhub/FirstSportsELM/tree/main/finetune
1. Convert TXT to Jsonl files
```python Json2Bin.py```
2. Convert Jsonl to Binidx for fine-tuning
```python make_data.py your_data.jsonl 3 1024```
3. Fine Tune the checkpoint with the following:
Note: put the pretrained .pth file under output
```
python train.py --load_model "0" --wandb "SportsRWKV_ft" --proj_dir "output/" --my_testing "x060" \
--ctx_len 1024 --my_pile_stage 3 --epoch_count 360 --epoch_begin 0 \
--data_file "data/test" --my_exit_tokens 1081350 --magic_prime 1049 \
--num_nodes 1 --micro_bsz 16 --n_layer 12 --n_embd 768 --pre_ffn 0 --head_qk 0 \
--lr_init 3e-3 --lr_final 3e-4 --warmup_steps 10 --beta1 0.9 --beta2 0.99 --adam_eps 1e-8 --my_pile_edecay 0 --data_type "binidx" --vocab_size 65536 \
--weight_decay 0.1 --epoch_save 10 --head_size_a 64 \
--accelerator gpu --devices 1 --precision bf16 --strategy deepspeed_stage_2 --grad_cp 0 --enable_progress_bar True --ds_bucket_mb 2
```
## Ask Your Question!
For inference, use: https://github.com/BlinkDL/ChatRWKV/blob/main/RWKV_v6_demo.py
## Cost
The entire pretrain and finetune process costs around 100 USD. ~50$ in GPU rentals and ~50$ in OpenAI API usage.
|
{"language": ["en"], "license": "apache-2.0", "tags": ["sports"], "datasets": ["Chrisneverdie/SportsRWKV"], "pipeline_tag": "text-generation"}
|
Chrisneverdie/SportsRWKV_150m
| null |
[
"transformers",
"RWKV-6",
"sports",
"text-generation",
"en",
"dataset:Chrisneverdie/SportsRWKV",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:35:00+00:00
|
token-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. -->
# finetuned__roberta-clinical-wl-es__augmented-ultrasounds-ner
This model is a fine-tuned version of [manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds](https://huggingface.co/manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3995
- Precision: 0.7932
- Recall: 0.8775
- F1: 0.8333
- Accuracy: 0.9231
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 22 | 1.2788 | 0.5687 | 0.2763 | 0.3719 | 0.6256 |
| No log | 2.0 | 44 | 0.6691 | 0.6975 | 0.7470 | 0.7214 | 0.8576 |
| No log | 3.0 | 66 | 0.4416 | 0.7649 | 0.8168 | 0.7900 | 0.9051 |
| No log | 4.0 | 88 | 0.3715 | 0.7350 | 0.8279 | 0.7787 | 0.9115 |
| No log | 5.0 | 110 | 0.3398 | 0.7658 | 0.8441 | 0.8031 | 0.9221 |
| No log | 6.0 | 132 | 0.3320 | 0.7808 | 0.8472 | 0.8126 | 0.9216 |
| No log | 7.0 | 154 | 0.3306 | 0.7844 | 0.8431 | 0.8127 | 0.9199 |
| No log | 8.0 | 176 | 0.3321 | 0.7778 | 0.8502 | 0.8124 | 0.9199 |
| No log | 9.0 | 198 | 0.3398 | 0.7845 | 0.8512 | 0.8165 | 0.9196 |
| No log | 10.0 | 220 | 0.3445 | 0.7731 | 0.8553 | 0.8121 | 0.9197 |
| No log | 11.0 | 242 | 0.3560 | 0.7804 | 0.8522 | 0.8147 | 0.9196 |
| No log | 12.0 | 264 | 0.3516 | 0.7904 | 0.8664 | 0.8267 | 0.9214 |
| No log | 13.0 | 286 | 0.3553 | 0.7923 | 0.8725 | 0.8304 | 0.9228 |
| No log | 14.0 | 308 | 0.3644 | 0.7896 | 0.8775 | 0.8313 | 0.9223 |
| No log | 15.0 | 330 | 0.3706 | 0.7927 | 0.8745 | 0.8316 | 0.9214 |
| No log | 16.0 | 352 | 0.3763 | 0.7921 | 0.8755 | 0.8317 | 0.9228 |
| No log | 17.0 | 374 | 0.3811 | 0.7869 | 0.8745 | 0.8284 | 0.9228 |
| No log | 18.0 | 396 | 0.3772 | 0.7830 | 0.8765 | 0.8271 | 0.9238 |
| No log | 19.0 | 418 | 0.3888 | 0.7829 | 0.8796 | 0.8284 | 0.9218 |
| No log | 20.0 | 440 | 0.3878 | 0.7900 | 0.8755 | 0.8305 | 0.9208 |
| No log | 21.0 | 462 | 0.3916 | 0.7853 | 0.8775 | 0.8289 | 0.9221 |
| No log | 22.0 | 484 | 0.3884 | 0.7938 | 0.8806 | 0.8349 | 0.9231 |
| 0.2377 | 23.0 | 506 | 0.3926 | 0.7921 | 0.8715 | 0.8299 | 0.9219 |
| 0.2377 | 24.0 | 528 | 0.3951 | 0.7956 | 0.8785 | 0.8350 | 0.9239 |
| 0.2377 | 25.0 | 550 | 0.3941 | 0.7920 | 0.8785 | 0.8330 | 0.9229 |
| 0.2377 | 26.0 | 572 | 0.3970 | 0.7934 | 0.8785 | 0.8338 | 0.9236 |
| 0.2377 | 27.0 | 594 | 0.3979 | 0.7965 | 0.8796 | 0.8360 | 0.9241 |
| 0.2377 | 28.0 | 616 | 0.3999 | 0.7949 | 0.8785 | 0.8346 | 0.9236 |
| 0.2377 | 29.0 | 638 | 0.4001 | 0.7925 | 0.8775 | 0.8329 | 0.9233 |
| 0.2377 | 30.0 | 660 | 0.3995 | 0.7932 | 0.8775 | 0.8333 | 0.9231 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds", "model-index": [{"name": "finetuned__roberta-clinical-wl-es__augmented-ultrasounds-ner", "results": []}]}
|
manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds-ner
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:manucos/finetuned__roberta-clinical-wl-es__augmented-ultrasounds",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:35:24+00:00
|
text-classification
|
transformers
|
{}
|
greasyFinger/french_xl
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:36:03+00:00
|
|
image-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. -->
# convnext-tiny-224-driverbox
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0497
- Accuracy: 0.9880
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.3349 | 0.9950 | 99 | 0.2700 | 0.9328 |
| 0.2393 | 2.0 | 199 | 0.1932 | 0.9540 |
| 0.1831 | 2.9950 | 298 | 0.1403 | 0.9618 |
| 0.1397 | 4.0 | 398 | 0.1055 | 0.9689 |
| 0.0795 | 4.9950 | 497 | 0.1030 | 0.9731 |
| 0.0915 | 6.0 | 597 | 0.0966 | 0.9703 |
| 0.0718 | 6.9950 | 696 | 0.0779 | 0.9745 |
| 0.0502 | 8.0 | 796 | 0.0729 | 0.9788 |
| 0.0314 | 8.9950 | 895 | 0.0621 | 0.9802 |
| 0.0408 | 10.0 | 995 | 0.0758 | 0.9752 |
| 0.0335 | 10.9950 | 1094 | 0.0598 | 0.9823 |
| 0.0228 | 12.0 | 1194 | 0.0573 | 0.9823 |
| 0.0229 | 12.9950 | 1293 | 0.0473 | 0.9844 |
| 0.0119 | 14.0 | 1393 | 0.0642 | 0.9844 |
| 0.028 | 14.9950 | 1492 | 0.0526 | 0.9851 |
| 0.0117 | 16.0 | 1592 | 0.0594 | 0.9837 |
| 0.0187 | 16.9950 | 1691 | 0.0497 | 0.9880 |
| 0.0131 | 18.0 | 1791 | 0.0663 | 0.9837 |
| 0.0132 | 18.9950 | 1890 | 0.0478 | 0.9866 |
| 0.014 | 20.0 | 1990 | 0.0465 | 0.9880 |
| 0.0039 | 20.9950 | 2089 | 0.0496 | 0.9851 |
| 0.0102 | 22.0 | 2189 | 0.0468 | 0.9880 |
| 0.0035 | 22.9950 | 2288 | 0.0581 | 0.9866 |
| 0.0071 | 24.0 | 2388 | 0.0519 | 0.9866 |
| 0.0032 | 24.9950 | 2487 | 0.0510 | 0.9880 |
| 0.0049 | 26.0 | 2587 | 0.0575 | 0.9858 |
| 0.0037 | 26.9950 | 2686 | 0.0511 | 0.9880 |
| 0.0029 | 28.0 | 2786 | 0.0484 | 0.9880 |
| 0.0019 | 28.9950 | 2885 | 0.0523 | 0.9866 |
| 0.0058 | 29.8492 | 2970 | 0.0532 | 0.9866 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/convnext-tiny-224", "model-index": [{"name": "convnext-tiny-224-driverbox", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9879688605803255, "name": "Accuracy"}]}]}]}
|
samayl24/convnext-tiny-224-driverbox
| null |
[
"transformers",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/convnext-tiny-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:38:10+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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": []}
|
El-chapoo/R_geema-100m
| null |
[
"transformers",
"safetensors",
"recurrent_gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:38:24+00:00
|
null |
peft
|
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
{"library_name": "peft"}
|
alexakkol/mistral-7B-v0.2-a1-adapter
| null |
[
"peft",
"safetensors",
"region:us"
] | null |
2024-04-23T23:38:26+00:00
|
null | null |
{"license": "unknown"}
|
girish787/Rice_leaf_disease_classifier
| null |
[
"license:unknown",
"region:us"
] | null |
2024-04-23T23:40:22+00:00
|
|
null | null |
{}
|
Jesaispas69/jm
| null |
[
"region:us"
] | null |
2024-04-23T23:40:41+00:00
|
|
text-to-image
|
diffusers
|
# 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 𧨠diffusers 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": "diffusers"}
|
rubbrband/realMINT_theos
| null |
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-23T23:41:49+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).
|
{"license": "mit"}
|
numen-tech/Phi-3-mini-4k-instruct-w4a16g128asym
| null |
[
"arxiv:2308.13137",
"license:mit",
"region:us"
] | null |
2024-04-23T23:43:24+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 π€ 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": []}
|
heyllm234/sc69
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:44:21+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** andrewatef
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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/tinyllama-bnb-4bit"}
|
andrewatef/ReSV01Main
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:45:18+00:00
|
text2text-generation
|
transformers
|
{}
|
himanshubeniwal/mbart-kk-en-c_MOC
| null |
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:49:33+00:00
|
|
video-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. -->
# videomae-base-ssbd-trim-yolo
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0946
- Accuracy: 0.9844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 9090
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6749 | 0.0333 | 303 | 0.7530 | 0.6452 |
| 0.9597 | 1.0333 | 606 | 1.4679 | 0.7137 |
| 0.5435 | 2.0333 | 909 | 0.4142 | 0.8992 |
| 0.5258 | 3.0333 | 1212 | 0.8016 | 0.8226 |
| 0.0066 | 4.0333 | 1515 | 0.3179 | 0.9194 |
| 1.5098 | 5.0333 | 1818 | 1.1273 | 0.7702 |
| 0.0016 | 6.0333 | 2121 | 0.1973 | 0.9637 |
| 0.2736 | 7.0333 | 2424 | 1.0816 | 0.7984 |
| 0.6896 | 8.0333 | 2727 | 0.4091 | 0.9194 |
| 0.2791 | 9.0333 | 3030 | 0.2331 | 0.9597 |
| 0.4142 | 10.0333 | 3333 | 0.3057 | 0.9315 |
| 0.3853 | 11.0333 | 3636 | 0.3853 | 0.9274 |
| 0.0004 | 12.0333 | 3939 | 0.1782 | 0.9718 |
| 0.0164 | 13.0333 | 4242 | 0.5571 | 0.9032 |
| 0.0002 | 14.0333 | 4545 | 0.1784 | 0.9597 |
| 0.1403 | 15.0333 | 4848 | 0.1136 | 0.9758 |
| 0.0003 | 16.0333 | 5151 | 0.1628 | 0.9677 |
| 0.2939 | 17.0333 | 5454 | 0.1729 | 0.9718 |
| 0.0001 | 18.0333 | 5757 | 0.1332 | 0.9718 |
| 0.0002 | 19.0333 | 6060 | 0.2212 | 0.9637 |
| 0.0007 | 20.0333 | 6363 | 0.4098 | 0.9274 |
| 0.02 | 21.0333 | 6666 | 0.3855 | 0.9395 |
| 0.0164 | 22.0333 | 6969 | 0.2359 | 0.9597 |
| 0.0002 | 23.0333 | 7272 | 0.2383 | 0.9677 |
| 0.0001 | 24.0333 | 7575 | 0.1351 | 0.9798 |
| 0.1064 | 25.0333 | 7878 | 0.1471 | 0.9798 |
| 0.0001 | 26.0333 | 8181 | 0.1395 | 0.9798 |
| 0.0001 | 27.0333 | 8484 | 0.1639 | 0.9758 |
| 0.0001 | 28.0333 | 8787 | 0.2662 | 0.9637 |
| 0.0001 | 29.0333 | 9090 | 0.2468 | 0.9677 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-ssbd-trim-yolo", "results": []}]}
|
Amit7Singh/videomae-base-ssbd-trim-yolo
| null |
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:52:18+00:00
|
null | null |
{"license": "apache-2.0"}
|
Norlo/Kymo
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-23T23:53:08+00:00
|
|
null | null |
3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B).
|
{"license": "other", "license_name": "llama3", "license_link": "LICENSE"}
|
numen-tech/Llama-3-Smaug-8B-w3a16g40sym
| null |
[
"arxiv:2308.13137",
"license:other",
"region:us"
] | null |
2024-04-23T23:53:22+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** andrewatef
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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/tinyllama-bnb-4bit"}
|
andrewatef/ReSV0116bit
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:53:52+00:00
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6836
- Mean Iou: 0.0785
- Mean Accuracy: 0.1421
- Overall Accuracy: 0.5276
- Per Category Iou: [0.5399621456272898, 0.4230438128529876, 0.8931988601700478, 0.5700974052454356, 0.6161933963343478, 0.5518067110797286, 0.672799915550537, 0.0, 0.13224736001488627, 0.07285965352749184, 0.07873415624223346, 0.06589184516376909, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10923639972706407, 0.018405220760681146, 0.04396316016693049, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
- Per Category Accuracy: [0.9092699968756467, 0.9865203285004562, 0.9656447761692465, 0.8933310207267366, 0.7559688399185908, 0.7905802456103311, 0.709918047173505, 0.0, 0.2928290614431149, 1.0, 0.133178257640514, 0.1504056128042096, 0.0, nan, 0.0, 0.0, 0.0, 0.153991896697467, 0.02513052558301427, 0.05050212836302021, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.812 | 1.0 | 20 | 4.9216 | 0.0108 | 0.0375 | 0.1423 | [0.11659967720641855, 0.23593711431627512, 0.4006311941130212, 0.11502801657635418, 0.07316850652312745, 0.03936552970718956, 0.27709561076055306, 0.0029129956733446615, 0.0, 0.04997225305216426, 0.0, 0.0, 0.0009024107257960552, 0.0, 0.0, 0.0, 0.0, 0.00290388430464909, 0.00012582233885753317, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.007426202116467604, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.00015842420721886305, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.00032397189543807074, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0] | [0.13689536662460286, 0.3924921423501977, 0.6422548381171834, 0.18205843287431137, 0.09005544248719208, 0.060540483479362396, 0.2961474070773759, 0.003968331161125917, 0.0, 0.17731613665452398, 0.0, 0.0, 0.033593281343731254, nan, 0.0, 0.0, 0.0, 0.0029440055965254904, 0.0004872955099199443, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.03879101341522547, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.00020564142989340918, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.003657142857142857, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 4.4984 | 2.0 | 40 | 4.6763 | 0.0242 | 0.0713 | 0.3436 | [0.4177555671956623, 0.3442832044316342, 0.6129024792432088, 0.22369935292322102, 0.3479776313773556, 0.05689757109933999, 0.2765786290383097, 0.0008884184261762093, 0.0, 0.0031787713357749985, 0.0, 0.0, 0.004341718809858256, 0.0, 0.0, 0.0, 0.0, 0.009323628036313078, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan] | [0.7184146138583329, 0.8374480381222752, 0.8843944630900499, 0.5385482612089151, 0.5100708821671697, 0.08296386220486295, 0.3110123266771977, 0.0009142723753574416, 0.0, 0.009057792655311607, 0.0, 0.0, 0.020395920815836834, nan, 0.0, 0.0, 0.0, 0.009968811029818987, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 4.5323 | 3.0 | 60 | 4.2979 | 0.0370 | 0.0847 | 0.4190 | [0.41879151861578867, 0.40886899708008034, 0.7798855206331494, 0.2990302618524624, 0.40161380454845647, 0.06021682613706414, 0.48480536921001444, 0.0011262792007301395, 0.0, 0.007760479041916167, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.02717957844956904, 0.0, 0.0006034517439755401, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan] | [0.8799599108950663, 0.800724931562405, 0.9452737564494315, 0.7071144603334718, 0.6657730367043301, 0.07905802456103311, 0.532494228198793, 0.0011282510163985449, 0.0, 0.01594959141478783, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.031159821610749993, 0.0, 0.0006199115592842088, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.7103 | 4.0 | 80 | 4.0054 | 0.0528 | 0.1050 | 0.4586 | [0.45956776399352367, 0.5197583595464864, 0.7120890080094743, 0.3102372932301349, 0.5014312977099237, 0.038685302529019236, 0.6536122265975282, 0.0010680233800027185, 0.0, 0.22662612281989444, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00384401395984017, 0.0, 0.006680887028615125, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.8764683446202288, 0.8426746426036703, 0.9537978593731737, 0.8668303748704864, 0.855610920064566, 0.04855026566733523, 0.6790270970877719, 0.0010698932052055168, 0.0, 0.6383774736634833, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00443058267991955, 0.0, 0.006860354589411911, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.5864 | 5.0 | 100 | 3.6505 | 0.0501 | 0.0990 | 0.4628 | [0.40192794658822223, 0.37222233525258897, 0.765344539597529, 0.4024280432121029, 0.4648372385595219, 0.019827651948448106, 0.5725238448200985, 0.0, 0.0, 0.14450347683412768, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.007402422611036339, 0.0, 0.007776556329139693, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9818442611310159, 0.9274612186961371, 0.9640000667902285, 0.7486363989063806, 0.8297845462839497, 0.02287202223864316, 0.5918155183820055, 0.0, 0.0, 0.36418233730432215, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.007695222549333955, 0.0, 0.007893540521552259, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.3341 | 6.0 | 120 | 3.5537 | 0.0578 | 0.1092 | 0.4975 | [0.43842804678092534, 0.5593515133048954, 0.6558938538150463, 0.35659964836230806, 0.4840409979216551, 0.04212073850457818, 0.8206441623792398, 0.0, 0.0, 0.15763715978363527, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0023493217731785764, 0.0, 0.008642228138439484, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9477238883185705, 0.8934198519720167, 0.9830686770525472, 0.8590983572885905, 0.8597375254403817, 0.051919490481719975, 0.858722507999514, 0.0, 0.0, 0.5422861081027862, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.002448479902060804, 0.0, 0.008720089267264537, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.491 | 7.0 | 140 | 3.3943 | 0.0590 | 0.1127 | 0.4928 | [0.42758989449573187, 0.3790973964868256, 0.8087944030435588, 0.4388023472353697, 0.5185999865156419, 0.0806907502977372, 0.6905984458067466, 0.0, 0.0, 0.2508111928550776, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.001760715832010391, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9604546136960288, 0.9802798337219912, 0.9477867388001135, 0.8399691449593544, 0.8637097340164223, 0.08940321615820401, 0.744504975225134, 0.0, 0.0, 0.768632470217584, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0017780627860203458, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.0307 | 8.0 | 160 | 3.3737 | 0.0608 | 0.1143 | 0.5075 | [0.435107983560956, 0.4937318516488977, 0.7833169107403758, 0.4323149757346965, 0.5513479805146095, 0.07870673310850489, 0.7738493983376752, 0.0, 0.0012170261964888794, 0.21652554744525548, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.002793811444084236, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.970434285111442, 0.9317955997161107, 0.9623553574112107, 0.9298869409235516, 0.8562706154817882, 0.08253281255498082, 0.8211551703187654, 0.0, 0.0013734141358649934, 0.7301368514325096, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0030897484478386336, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.8834 | 9.0 | 180 | 3.1410 | 0.0612 | 0.1176 | 0.5138 | [0.43602241880633563, 0.6124982092414163, 0.8285314384151593, 0.3945496641138368, 0.48551927829766395, 0.09774110380581737, 0.7177494962943013, 0.0, 0.0, 0.16236753780593582, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00015133553610613664, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9616718942102082, 0.8452854101186251, 0.9637078595401493, 0.8843848846815677, 0.8906309214681732, 0.12306027657553045, 0.9535083100436091, 0.0, 0.0, 0.8477897016835679, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0001748914215757717, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.5405 | 10.0 | 200 | 3.1862 | 0.0630 | 0.1215 | 0.5182 | [0.45919041519012765, 0.5500077024986906, 0.8097353322228391, 0.39275574060492663, 0.4853838597326824, 0.11822330109965909, 0.8134110787172012, 0.0, 0.022053293530956763, 0.2532786442873334, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9461454812518513, 0.904978201358613, 0.9496735627577685, 0.9312973266572032, 0.9154607340866026, 0.15680530630915937, 0.9209972052330998, 0.0, 0.032721591786983466, 0.924091759377769, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.3467 | 11.0 | 220 | 3.0653 | 0.0666 | 0.1160 | 0.5105 | [0.41898992303278876, 0.4698276287357796, 0.8623409746934038, 0.4433330262591292, 0.6074328453770262, 0.12934667667928462, 0.7687961103679891, 0.0, 0.0, 0.2244019283830084, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.003386004514672686, 0.0, 4.132743728561392e-05, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9570969482777509, 0.9655784244144784, 0.9428025179916178, 0.9445248278097029, 0.8788827286125342, 0.1484922059185756, 0.8128451267096007, 0.0, 0.0, 0.7286600374126219, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0037601655638790916, 0.0, 4.132743728561392e-05, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.7165 | 12.0 | 240 | 2.9867 | 0.0702 | 0.1237 | 0.5322 | [0.4375705471850902, 0.5962226794126263, 0.8374875517773896, 0.4075869574144799, 0.6016851491508133, 0.28791147007465795, 0.8257287687797833, 0.0, 0.0010601174963558461, 0.212303947332024, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.004641860694797659, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9351250349968148, 0.8976376356078273, 0.9689091485915611, 0.9060381853949765, 0.8940697592813531, 0.4684981878320842, 0.9726396370853416, 0.0, 0.0012360727222784941, 0.7556365068425717, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00547993120937418, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.6604 | 13.0 | 260 | 3.0308 | 0.0669 | 0.1228 | 0.5175 | [0.45501984614428337, 0.5177541669958132, 0.86240930190635, 0.40943696244007033, 0.5610606086941408, 0.1707078180601064, 0.8103946278528834, 0.0, 0.0049765158602332014, 0.20923459116803014, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.011475755817021052, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9356504944187688, 0.9303406671398154, 0.958573360717327, 0.9308271980793194, 0.9061548178819566, 0.21311446567437278, 0.8949194648088893, 0.0, 0.008858521176329208, 0.9618981982868957, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.01267962806424345, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.068 | 14.0 | 280 | 2.8922 | 0.0709 | 0.1275 | 0.5297 | [0.4287012181397979, 0.5046371883557643, 0.8635682839377298, 0.4858201939870335, 0.5720581034764158, 0.31352863075228116, 0.8091911565341933, 0.0, 0.0, 0.20297746507596615, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9506169583406032, 0.985572341072696, 0.9531132595301307, 0.9128755893723556, 0.8855358270755842, 0.48816812695731726, 0.8562483967218869, 0.0, 0.0, 0.9799153293295264, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.508 | 15.0 | 300 | 2.8214 | 0.0712 | 0.1323 | 0.5363 | [0.4554859333784407, 0.594493470039186, 0.852014424640123, 0.457716544625097, 0.560047009948617, 0.4120042593200947, 0.7380026129883643, 0.0, 0.09210099981484911, 0.10518340474448741, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.006157769097222222, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9423779980604664, 0.9705921119334888, 0.9625974719898479, 0.9127067082327274, 0.8628394975085971, 0.6398976037158239, 0.8522621410344688, 0.0, 0.13663753884186852, 0.9909422073446884, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.006616725449616697, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.8156 | 16.0 | 320 | 3.0021 | 0.0765 | 0.1343 | 0.5339 | [0.506177917119982, 0.5606776771194927, 0.8608020757122061, 0.4038688149943858, 0.6289082093295435, 0.4639290961527997, 0.8084261412311835, 0.0, 0.10154023684043878, 0.1296733771569433, 0.010588898432467254, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0416435872903973, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.8938835711764205, 0.9735628105039035, 0.9528127035014777, 0.9292251094304141, 0.8447469997894589, 0.5999947218410219, 0.891847921476501, 0.0, 0.2477467424333465, 0.9943881067244265, 0.011056149545282569, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.048211735214387735, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.3716 | 17.0 | 340 | 2.7714 | 0.0718 | 0.1282 | 0.5327 | [0.4286983159080724, 0.5330586775433281, 0.8708396572076837, 0.5199412897336303, 0.6777541662006487, 0.259636999824071, 0.8151670892880853, 0.0, 0.036764389957463874, 0.14084188824717228, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.025910555343198822, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9664781234403593, 0.9865152590489709, 0.9484880362003039, 0.9280931493593928, 0.8505579338900976, 0.4673721805834125, 0.8496125129949911, 0.0, 0.04406942608456797, 0.9819828689573693, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.027661993179234557, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.0282 | 18.0 | 360 | 2.8218 | 0.0744 | 0.1344 | 0.5345 | [0.49902449481607236, 0.5230612482691531, 0.8446968039327236, 0.44448923466942575, 0.6122017696318534, 0.48659411003519476, 0.7491900335858783, 0.0, 0.097234409276089, 0.10501447477253929, 0.004101688271691352, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.02454309535641323, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9350966317848173, 0.9785562202169725, 0.9668970929553007, 0.9059149478065991, 0.8701522913888694, 0.684744361166825, 0.8093921719524214, 0.0, 0.2130680355027554, 1.0, 0.004105769096030156, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.025913078963476843, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.8272 | 19.0 | 380 | 2.8569 | 0.0737 | 0.1323 | 0.5276 | [0.46075370606113414, 0.4734631279317232, 0.8789637098633548, 0.4979618662216249, 0.6595972897771925, 0.4454093338631324, 0.7159488459057832, 0.0, 0.09882716610600964, 0.15156986808332837, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.04182247328082319, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.949454455449562, 0.9890905404035283, 0.9548414566948855, 0.9233644774498035, 0.8717664397501579, 0.611377951370562, 0.7418688484750293, 0.0, 0.18661264571065597, 1.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.04596729530416533, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.6888 | 20.0 | 400 | 2.7458 | 0.0715 | 0.1321 | 0.5249 | [0.4723918867202449, 0.5046992530330829, 0.8746281148944265, 0.4598547747304244, 0.6027563963036983, 0.500496781991811, 0.68147144108263, 0.0, 0.1031883775351014, 0.0699291551632736, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.02107997265892003, 0.0, 0.00041118421052631577, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9391014846764671, 0.9789364290783737, 0.9596753994890548, 0.8868359433837391, 0.855119657519826, 0.6957229318413737, 0.743279733214522, 0.0, 0.18168552249824033, 1.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.022473547672486664, 0.0, 0.0004132743728561392, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.4588 | 21.0 | 420 | 2.7440 | 0.0799 | 0.1392 | 0.5460 | [0.5302858359159093, 0.5111650103573957, 0.8549338910883694, 0.4869585367951303, 0.6689821634974956, 0.5047567676504235, 0.7992149854804225, 0.0, 0.10519262751350386, 0.10555907754024589, 0.08517486031514339, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05782516880882238, 0.0, 0.0023626216953847408, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9300104280364048, 0.9882540809084457, 0.9555177077593549, 0.9303753269219358, 0.8286195522492806, 0.749111509905345, 0.8453494808754236, 0.0, 0.2627856272210682, 1.0, 0.09975757745610471, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.06465152884251027, 0.0, 0.002396991362565607, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.2493 | 22.0 | 440 | 2.6866 | 0.0769 | 0.1323 | 0.5395 | [0.44833863414034775, 0.5466934014469449, 0.8794700154476356, 0.5038024596249997, 0.6580129981953504, 0.38456344027374667, 0.839884864751146, 0.0, 0.1103142509135201, 0.0958108120855383, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0651696861444246, 0.0, 0.00449934554973822, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9623596577007194, 0.9841174084964007, 0.9553841273021757, 0.9074257493530027, 0.7830303880974103, 0.521015869664661, 0.8893535582648143, 0.0, 0.19435526790159488, 1.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07444544845075349, 0.0, 0.004546018101417531, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.4216 | 23.0 | 460 | 2.7148 | 0.0742 | 0.1314 | 0.5260 | [0.4639948414002914, 0.5470656273001384, 0.8774763581141714, 0.5234877283142824, 0.6780880953713551, 0.3276870767028247, 0.7241370664961797, 0.0, 0.1097809667673716, 0.08896070909313854, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.09638674308497383, 0.0, 0.014387614492988572, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9678861112350934, 0.9758795498327081, 0.9551670590592596, 0.9019804736887749, 0.8502631763632535, 0.41881311798444704, 0.7799087312838376, 0.0, 0.249532180809971, 1.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.11274666977584749, 0.0, 0.014671240236392942, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 3.3308 | 24.0 | 480 | 2.6179 | 0.0787 | 0.1408 | 0.5444 | [0.5202341682633934, 0.5742785924278294, 0.8614114203798829, 0.4273012268384482, 0.6274095976429042, 0.5280709520982785, 0.7361818140681916, 0.0, 0.12214983713355049, 0.09642752034025424, 0.07442208442702984, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07311541405607688, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.8962187209627878, 0.9764017033356991, 0.9632319791614486, 0.8622751484556505, 0.8428661660467401, 0.9234227101586966, 0.8786234085355151, 0.0, 0.24592696870332537, 1.0, 0.0822414976948839, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0751741627073192, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.1328 | 25.0 | 500 | 2.7226 | 0.0703 | 0.1291 | 0.5052 | [0.49116275942915605, 0.5360527172683272, 0.8781517738511584, 0.4964388439939734, 0.6041372236152268, 0.3896924055209905, 0.5394067769370713, 0.0, 0.14028158295281584, 0.04736235911831497, 0.024812933256588003, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.06781224468281369, 0.0, 0.00012372154404486968, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.948634819903348, 0.9847815066409814, 0.9601178847534606, 0.9264362884489865, 0.744010105972349, 0.5498873992751329, 0.5664364696828547, 0.0, 0.3164517845799928, 1.0, 0.02778750893320068, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07499927128574344, 0.0, 0.00012398231185684175, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.8864 | 26.0 | 520 | 2.7677 | 0.0747 | 0.1343 | 0.5142 | [0.5239320913532985, 0.5623231891516277, 0.867788824755976, 0.49360503413309914, 0.5913540915750064, 0.49843740872714526, 0.5863471137407134, 0.0, 0.0913083506666212, 0.05097923086961323, 0.05438696432602083, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07740565346873017, 0.0, 0.006898510733271111, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.8940823936604031, 0.9804623339754639, 0.9589741020888644, 0.9029618100406684, 0.7138746578707278, 0.900744220415919, 0.6332104716000378, 0.0, 0.22985802331370495, 1.0, 0.0709050908734218, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.09243011630279535, 0.0, 0.007025664338554366, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.0412 | 27.0 | 540 | 2.7090 | 0.0780 | 0.1351 | 0.5367 | [0.4806058633532184, 0.5495949818362328, 0.8754918194661765, 0.5211248861622928, 0.6545745793740737, 0.40775497988094844, 0.7828466462144453, 0.0, 0.13429254624189585, 0.08920055854637428, 0.022661136952088535, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0664292028495658, 0.0, 0.01693175987685993, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.946691228682375, 0.9878333164351617, 0.9622969159611949, 0.9115291046104551, 0.8431328514281704, 0.5393222843872058, 0.83719470209405, 0.0, 0.28057134028051983, 1.0, 0.027717444614155796, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07447459702101612, 0.0, 0.01772947059552837, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.55 | 28.0 | 560 | 2.6887 | 0.0804 | 0.1410 | 0.5369 | [0.5240697938223876, 0.5359399597089356, 0.8767672185680805, 0.5001088310031687, 0.6370467760033549, 0.5767559051256429, 0.69926689197774, 0.0, 0.15391199696230556, 0.07914997740130605, 0.05915808613654143, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.09441707717569786, 0.0, 0.005985342019543974, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9232362619749971, 0.9872097739024638, 0.9682078511913706, 0.9019028796516484, 0.7889115025615833, 0.8276241247053028, 0.7430637125845518, 0.0, 0.4105649882401415, 1.0, 0.07841598587503328, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.1173229953070802, 0.0, 0.006075133280985246, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.8633 | 29.0 | 580 | 2.6442 | 0.0774 | 0.1361 | 0.5446 | [0.4765311711281216, 0.5489176633389344, 0.8727707090638301, 0.5248802243852118, 0.6541019955654102, 0.4560701852434012, 0.809037732581632, 0.0, 0.08763090142313341, 0.10848250523347716, 0.001047159579640226, 0.002069378641834123, 0.0, nan, 0.0, 0.0, 0.0, 0.09656273433153528, 0.0, 0.00700503871205604, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9549504769710815, 0.9853442157558552, 0.9638163936616073, 0.9335886329299965, 0.7908625166678364, 0.6891604208452092, 0.8515229454412896, 0.0, 0.19049254064447457, 1.0, 0.0011770805599540378, 0.0027771687495432287, 0.0, nan, 0.0, 0.0, 0.0, 0.11636109248841345, 0.0, 0.00706699177583998, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.7698 | 30.0 | 600 | 2.7209 | 0.0772 | 0.1368 | 0.5316 | [0.5022697875929844, 0.5372854983305001, 0.8766196240647242, 0.5297488423859973, 0.6677027182016286, 0.4185004025273841, 0.7164081774259868, 0.0, 0.11446649232180262, 0.09481623927634589, 0.07439389923128684, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.08754294146127775, 0.0, 0.011770935158031933, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9398622849978292, 0.9837777552468823, 0.9624972866469635, 0.9211370721487615, 0.8171380447750719, 0.6036366515359443, 0.752747512387433, 0.0, 0.31710415629452865, 1.0, 0.10225186721410255, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.11067712128720086, 0.0, 0.011943629375542423, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.718 | 31.0 | 620 | 2.7071 | 0.0785 | 0.1386 | 0.5269 | [0.5257928681715949, 0.5248190390853984, 0.8841481825571943, 0.5118205392885017, 0.5966746776355317, 0.5521120061829315, 0.6391115548430245, 0.0, 0.16269878007961885, 0.05863066204101895, 0.06883714214998762, 0.008988112496375761, 0.0, nan, 0.0, 0.0, 0.0, 0.08699937281605591, 0.003799977971142196, 0.004948304300599429, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9167927904532747, 0.9857903274865659, 0.9534388618945049, 0.9278877533787638, 0.7248368306547828, 0.8169534466378128, 0.6741295043676672, 0.0, 0.3922127418496455, 1.0, 0.09353586592491908, 0.013593510195132646, 0.0, nan, 0.0, 0.0, 0.0, 0.11321304690004955, 0.004803341454925165, 0.005083274786130512, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.444 | 32.0 | 640 | 2.6372 | 0.0756 | 0.1315 | 0.5257 | [0.4807000058745718, 0.5269885286566938, 0.8828004563112782, 0.5470558228668331, 0.6272312396025359, 0.43296372961356416, 0.694046733901461, 0.0, 0.09268953874374021, 0.06819524640794951, 0.024095833094514817, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07916781275787052, 0.0, 0.005333007653476633, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9628648291141039, 0.9865152590489709, 0.9561939588238241, 0.9055589281068425, 0.7832268931153064, 0.5777648756113868, 0.7372682841211335, 0.0, 0.1960548678947278, 1.0, 0.029455039726468898, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0922843734514822, 0.0, 0.005413894284415423, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.9673 | 33.0 | 660 | 2.6539 | 0.0772 | 0.1359 | 0.5354 | [0.5148575384189396, 0.5194958781821288, 0.8804693639325252, 0.5299236004413962, 0.5956744518589132, 0.4847076155517453, 0.7428720334566348, 0.0, 0.10384181352335393, 0.08459584391787782, 0.0699504426980737, 0.004923108156118358, 0.0, nan, 0.0, 0.0, 0.0, 0.09190302979331806, 0.00039154267815191856, 0.006111875657734963, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9430921359621183, 0.9865101895974856, 0.9634824425186596, 0.9162212616790437, 0.7016492385430556, 0.6888437313065203, 0.7890288522553904, 0.0, 0.251180277773009, 1.0, 0.09454479211916539, 0.007089088650149821, 0.0, nan, 0.0, 0.0, 0.0, 0.1274084006179497, 0.0004872955099199443, 0.006240443030127702, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.4463 | 34.0 | 680 | 2.6223 | 0.0777 | 0.1343 | 0.5381 | [0.4931988738076929, 0.5809856094190713, 0.8808855125844076, 0.5269065015315662, 0.589950165157043, 0.41765774106252673, 0.7756592304819765, 0.0, 0.09481459956718233, 0.07694871853148177, 0.04947933303520092, 0.0066823747537564215, 0.0, nan, 0.0, 0.0, 0.0, 0.08745712885840275, 0.0, 0.0026124351915115954, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9602720216189019, 0.9826067119537666, 0.9584147339244269, 0.9068369475418665, 0.7194750508807636, 0.6191016573419191, 0.8343020508458557, 0.0, 0.21286202338237567, 1.0, 0.06511777812031445, 0.012643426149236279, 0.0, nan, 0.0, 0.0, 0.0, 0.11446643542134258, 0.0, 0.0026862834235649048, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.6736 | 35.0 | 700 | 2.6612 | 0.0777 | 0.1361 | 0.5386 | [0.49017432679179485, 0.5199704713654861, 0.8895260440383116, 0.5397627574454489, 0.6175130737943056, 0.455614444024846, 0.7688663293575954, 0.0, 0.11115204916747672, 0.09799324650265316, 0.03229496064051672, 0.04120343280670958, 0.0, nan, 0.0, 0.0, 0.0, 0.09298528351499215, 0.0, 0.006056201550387597, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9672734133762898, 0.9890905404035283, 0.962238474511179, 0.9252678135369644, 0.7458347954242402, 0.6155740877581899, 0.7980983433917939, 0.0, 0.24342048790537177, 1.0, 0.04035704776985272, 0.061755462983263905, 0.0, nan, 0.0, 0.0, 0.0, 0.12781648060162648, 0.0, 0.006199115592842088, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.9268 | 36.0 | 720 | 2.7042 | 0.0778 | 0.1399 | 0.5288 | [0.5326911443613713, 0.4719542236071391, 0.8914294590501339, 0.539317248779064, 0.6171145304754427, 0.5459458880945246, 0.6445782747195108, 0.0, 0.12342652859588654, 0.07507298865442182, 0.07719188072279394, 0.029593255692682077, 0.0, nan, 0.0, 0.0, 0.0, 0.10203915833942644, 0.003959858963927312, 0.013666979949874687, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9146990679688863, 0.9897090134847409, 0.9582394095743793, 0.9108125008558166, 0.7391255526703628, 0.8974805587810971, 0.6802590897430705, 0.0, 0.2809490291678827, 1.0, 0.10679203508821097, 0.0497697873273405, 0.0, nan, 0.0, 0.0, 0.0, 0.14644241699944618, 0.005081796032022277, 0.014423275612679257, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.743 | 37.0 | 740 | 2.6692 | 0.0796 | 0.1374 | 0.5313 | [0.49626376672799216, 0.5109907112623977, 0.8885434826208365, 0.5732370278514043, 0.6786892482111327, 0.5400390206907518, 0.6568889628170024, 0.0, 0.10615209046581596, 0.07972026874293608, 0.037273200270111696, 0.017193105258552804, 0.0, nan, 0.0, 0.0, 0.0, 0.09007798440311937, 0.0, 0.021038583820701024, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9587909969933172, 0.9877927608232789, 0.9616874551253152, 0.8903961403813062, 0.8200856200435118, 0.7767514690875823, 0.6931359444826981, 0.0, 0.263489501965699, 1.0, 0.04718131244482435, 0.028721771541328656, 0.0, nan, 0.0, 0.0, 0.0, 0.10942373276590783, 0.0, 0.02215150638508906, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.2205 | 38.0 | 760 | 2.6895 | 0.0791 | 0.1397 | 0.5322 | [0.5262248520440836, 0.5076380728554641, 0.8881528662420383, 0.5330287102823486, 0.6228258055405183, 0.5312174722886726, 0.6783169533169533, 0.0, 0.12135954830502801, 0.07727656595935696, 0.08425889356619241, 0.06988072475644178, 0.0, nan, 0.0, 0.0, 0.0, 0.09700418628990058, 0.0062185219447014085, 0.0038901045465596887, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9385821116570839, 0.9876913717935719, 0.9604267895606873, 0.9211325077936364, 0.7428451119376799, 0.767708223371688, 0.7082000081007737, 0.0, 0.2800734776562688, 1.0, 0.12257051973711867, 0.11218300080391727, 0.0, nan, 0.0, 0.0, 0.0, 0.12968198909843473, 0.008423250957187609, 0.003967433979418936, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.7074 | 39.0 | 780 | 2.6644 | 0.0760 | 0.1339 | 0.5259 | [0.4885914966978212, 0.5200370522629109, 0.885025367115342, 0.5612080540739326, 0.655865603644647, 0.4317312687375214, 0.6703385346084847, 0.0, 0.10274548602522879, 0.07567201096674216, 0.029055022604476788, 0.028387680249208663, 0.0, nan, 0.0, 0.0, 0.0, 0.09453132315759903, 0.0, 0.018137044130868155, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9688376188370103, 0.9875747744094089, 0.9655863347192306, 0.8985389499244599, 0.8082672468243386, 0.6068035469228333, 0.7012367181065792, 0.0, 0.21394358701436936, 1.0, 0.03692389613665345, 0.04129211430241906, 0.0, nan, 0.0, 0.0, 0.0, 0.11770192672049436, 0.0, 0.019175930900524857, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.3343 | 40.0 | 800 | 2.6647 | 0.0780 | 0.1374 | 0.5244 | [0.5244626331481261, 0.5503707045514518, 0.885295991770116, 0.5355035699331692, 0.5827763963854691, 0.528928985757658, 0.6284643474918317, 0.0, 0.11526283815173498, 0.05971158311826503, 0.08411061721197989, 0.05415066860849994, 0.0, nan, 0.0, 0.0, 0.0, 0.1065540403012416, 0.0020264926848556743, 0.022319408837279446, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9471720544854758, 0.9833265740646862, 0.9627477500041743, 0.8989725636613431, 0.6743911853463401, 0.754020197755023, 0.6622179918182186, 0.0, 0.27686312211368436, 1.0, 0.1293947844120903, 0.08967331725498794, 0.0, nan, 0.0, 0.0, 0.0, 0.15259276532486082, 0.002854159415245388, 0.02446584287308344, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.7256 | 41.0 | 820 | 2.6421 | 0.0794 | 0.1371 | 0.5282 | [0.5058624383972573, 0.5076158949035198, 0.8874206373667016, 0.5736626597642654, 0.6215335366914485, 0.5041646593473466, 0.6600796494650866, 0.0, 0.12029710963991604, 0.07057294923639193, 0.06998106328255278, 0.035934120778572616, 0.0, nan, 0.0, 0.0, 0.0, 0.09655049786628735, 0.0012348728081007657, 0.02943719688710102, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9579429582351056, 0.9864696339856027, 0.961553874668136, 0.895257178589523, 0.7521650642150326, 0.6996639572117246, 0.6920355893987876, 0.0, 0.26858830194509775, 1.0, 0.10408755237307848, 0.06314404735803551, 0.0, nan, 0.0, 0.0, 0.0, 0.12662138922085872, 0.0013922728854855553, 0.0323593833946357, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 0.9674 | 42.0 | 840 | 2.6555 | 0.0811 | 0.1399 | 0.5320 | [0.5180040789717115, 0.561033681765389, 0.8853632708186255, 0.5624835982337437, 0.6070831505436265, 0.5314716078058738, 0.660800286341571, 0.0, 0.11243005721872727, 0.06440097644485306, 0.07479633074603888, 0.07617837251896983, 0.0, nan, 0.0, 0.0, 0.0, 0.10766879401862982, 0.0005809040980143641, 0.021305337913466362, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9543174911037082, 0.9795194159991889, 0.9632570254971697, 0.9000497514708634, 0.7186749947364728, 0.7970811780850839, 0.6979255268878178, 0.0, 0.24422736871019246, 1.0, 0.11437299440886733, 0.1489439450412921, 0.0, nan, 0.0, 0.0, 0.0, 0.15195149677908298, 0.0007657500870170554, 0.022812745381658884, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.0216 | 43.0 | 860 | 2.6680 | 0.0798 | 0.1409 | 0.5269 | [0.5207102119633555, 0.5079455684701409, 0.8916668599392351, 0.5667808170915599, 0.6053462556778159, 0.5175483776028253, 0.6497516130502439, 0.0, 0.11202810670919787, 0.06758851986664625, 0.08740505611608661, 0.11571191365711914, 0.0, nan, 0.0, 0.0, 0.0, 0.11400020187746038, 0.0002731941864277128, 0.028472038416455552, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9409720390665893, 0.9834228936429078, 0.9629314231327957, 0.9178507364586994, 0.6995999719278546, 0.74253140504592, 0.673687337140697, 0.0, 0.2556438737145702, 1.0, 0.12964701596065187, 0.24446393334795002, 0.0, nan, 0.0, 0.0, 0.0, 0.1646019762730638, 0.0003480682213713888, 0.03111956027606728, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.3687 | 44.0 | 880 | 2.6834 | 0.0780 | 0.1376 | 0.5228 | [0.4993664858335674, 0.5013679767095841, 0.8900906730761823, 0.5933372477151279, 0.6049721076885762, 0.4703812428275245, 0.638617876909345, 0.0, 0.08956949737925159, 0.06334505812503119, 0.06901137288371077, 0.10750378534455439, 0.0, nan, 0.0, 0.0, 0.0, 0.10403136385236814, 0.0006135345726731702, 0.04696958646342353, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9595193365009678, 0.9856534522964615, 0.964617876404682, 0.9132544308477377, 0.7002035230542494, 0.6526355607164221, 0.6617015674996962, 0.0, 0.2021322254459304, 1.0, 0.09744545492762356, 0.22312358400935467, 0.0, nan, 0.0, 0.0, 0.0, 0.15314658815985077, 0.0006961364427427776, 0.053229739223870726, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.2053 | 45.0 | 900 | 2.6912 | 0.0814 | 0.1414 | 0.5286 | [0.5131558211271191, 0.4742907174405588, 0.8912158679110277, 0.6021033054564809, 0.6249620087438337, 0.5172639942036952, 0.663308250670872, 0.0, 0.12338347947852003, 0.07140848437126507, 0.06716766644096245, 0.10460167575475462, 0.0, nan, 0.0, 0.0, 0.0, 0.10154801341750526, 0.008047529030515798, 0.040435915312075736, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9446786582322653, 0.9858663692588462, 0.9670724173053482, 0.8984294054014579, 0.7504105551266755, 0.7285178929589359, 0.6899800180917277, 0.0, 0.2828718089580937, 1.0, 0.09869259980662248, 0.229920339106921, 0.0, nan, 0.0, 0.0, 0.0, 0.14207013146005187, 0.010372432996867386, 0.04554283588874654, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.0914 | 46.0 | 920 | 2.6767 | 0.0824 | 0.1433 | 0.5417 | [0.5124237729505224, 0.5090064663829474, 0.8940940632228219, 0.5785617407283175, 0.6320464276867547, 0.5199316345556246, 0.7598974365606974, 0.0, 0.1181433607520564, 0.09844614832837355, 0.08011084294270135, 0.09720695674662184, 0.0, nan, 0.0, 0.0, 0.0, 0.1068487944939408, 0.01133547740812991, 0.026999432785025524, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9461556252561361, 0.9828500456250634, 0.9681577585199286, 0.9053717895467139, 0.7520948838514984, 0.7359249093916042, 0.7932513805068384, 0.0, 0.25890573228724956, 0.9999015457320075, 0.12558328545604866, 0.21976174815464444, 0.0, nan, 0.0, 0.0, 0.0, 0.14752091409916343, 0.014966933518969718, 0.02950779022192834, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.4734 | 47.0 | 940 | 2.6619 | 0.0822 | 0.1414 | 0.5330 | [0.51273550756626, 0.5016545026014111, 0.889094836169789, 0.5789903600885845, 0.611114438707668, 0.5261948842422028, 0.7027061768703985, 0.0, 0.11184017318487317, 0.07722486219349933, 0.08220253052947141, 0.09310727301536999, 0.0, nan, 0.0, 0.0, 0.0, 0.10257165086107198, 0.009116871704745167, 0.048316251830161056, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9425889933495908, 0.9829666430092264, 0.9691429143916245, 0.8961746139696652, 0.7160362130675837, 0.7475720468700517, 0.7380682355164918, 0.0, 0.22882796271180622, 1.0, 0.13064192929108923, 0.2147189943725791, 0.0, nan, 0.0, 0.0, 0.0, 0.1421867257411024, 0.011555864949530108, 0.054552217217010375, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.367 | 48.0 | 960 | 2.6827 | 0.0807 | 0.1396 | 0.5255 | [0.5107998872074443, 0.4610670912760284, 0.8959695073669829, 0.5913794094251226, 0.616647265629593, 0.5342724628768369, 0.6596931254818098, 0.0, 0.11261708712677837, 0.07040613324275802, 0.07085716321912805, 0.08489231758277081, 0.0, nan, 0.0, 0.0, 0.0, 0.09449944525049074, 0.007647858599592114, 0.04882959979864083, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9371518070529233, 0.9879144276589273, 0.9635909766401175, 0.892450100187595, 0.7361077970383887, 0.7336728948942609, 0.6903344269377726, 0.0, 0.23550618894744973, 1.0, 0.1114583187366002, 0.1930132280932544, 0.0, nan, 0.0, 0.0, 0.0, 0.12909901769318216, 0.009397841977027498, 0.0561226598338637, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 2.0933 | 49.0 | 980 | 2.6558 | 0.0805 | 0.1401 | 0.5269 | [0.5154490856937971, 0.4541122913505311, 0.8921043505067242, 0.5739374574160656, 0.6159028238332926, 0.544718983261163, 0.6631041592514566, 0.0, 0.11673884238761593, 0.06969738557606532, 0.07422505686633067, 0.07410361107566671, 0.0, nan, 0.0, 0.0, 0.0, 0.09939109476087783, 0.007983682983682983, 0.05094509450945094, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9333052006281167, 0.9861046334786576, 0.9664128637980264, 0.8920027933853365, 0.7429995087374552, 0.7491730884267568, 0.69799978397937, 0.0, 0.24805576061391613, 1.0, 0.11660103975449462, 0.16977271066286634, 0.0, nan, 0.0, 0.0, 0.0, 0.13702742880461713, 0.009537069265576053, 0.0584783237591437, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
| 1.4343 | 50.0 | 1000 | 2.6836 | 0.0785 | 0.1421 | 0.5276 | [0.5399621456272898, 0.4230438128529876, 0.8931988601700478, 0.5700974052454356, 0.6161933963343478, 0.5518067110797286, 0.672799915550537, 0.0, 0.13224736001488627, 0.07285965352749184, 0.07873415624223346, 0.06589184516376909, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10923639972706407, 0.018405220760681146, 0.04396316016693049, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] | [0.9092699968756467, 0.9865203285004562, 0.9656447761692465, 0.8933310207267366, 0.7559688399185908, 0.7905802456103311, 0.709918047173505, 0.0, 0.2928290614431149, 1.0, 0.133178257640514, 0.1504056128042096, 0.0, nan, 0.0, 0.0, 0.0, 0.153991896697467, 0.02513052558301427, 0.05050212836302021, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan] |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["scene_parse_150"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-scene-parse-150", "results": []}]}
|
gost-sniper/segformer-b0-scene-parse-150
| null |
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"dataset:scene_parse_150",
"base_model:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:54:30+00:00
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** andrewatef
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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/tinyllama-bnb-4bit"}
|
andrewatef/ReSV014bit
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null |
2024-04-23T23:56:04+00:00
|
text-generation
|
transformers
|
{}
|
winglian/llama-3-32k-merged
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-23T23:56:37+00:00
|
|
text-classification
|
transformers
|
# Malicious-Url-Detection
Using this model, you can detects harmful links created to harm people such as phishing, malware urls. this model Classifies if the urls addresses are malware and benign.
Type the domain name of the url address in the text field for classification in API: Like this:
"huggingface.com"
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an [kmack/Phishing_urls](https://huggingface.co/datasets/kmack/Phishing_urls) dataset.
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["distilbert", "bert"], "datasets": ["kmack/Phishing_urls"], "pipeline_tag": "text-classification"}
|
kmack/malicious-url-detection
| null |
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"bert",
"en",
"dataset:kmack/Phishing_urls",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null |
2024-04-23T23:57:02+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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/prithviraj/huggingface/runs/eccgtrnb)
[<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/prithviraj/huggingface/runs/eccgtrnb)
[<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/prithviraj/huggingface/runs/eccgtrnb)
# phi-3-mini-general-ift
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/Phi-3-mini-4k-instruct", "model-index": [{"name": "phi-3-mini-general-ift", "results": []}]}
|
prithviraj-maurya/phi-3-mini-general-ift
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null |
2024-04-23T23:57:41+00:00
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# idefics2-8b-docvqa-finetuned-tutorial
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceM4/idefics2-8b", "model-index": [{"name": "idefics2-8b-docvqa-finetuned-tutorial", "results": []}]}
|
zesquirrelnator/idefics2-8b-docvqa-finetuned-tutorial
| null |
[
"transformers",
"safetensors",
"idefics2",
"pretraining",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-23T23:58:04+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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"}
|
bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Epistemic_Llama13b_0.0_Seed102
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null |
2024-04-23T23:59:41+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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"}
|
bmehrba/Llama-2-13b-chat-hf-fine-tuned_Epistemic_Llama13b_0.0_Seed102
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null |
2024-04-24T00:00:01+00:00
|
null | null |
{}
|
Takekazuchi/mobilevitv2-1.0-imagenet1k-256-finetuned-eurosat
| null |
[
"region:us"
] | null |
2024-04-24T00:00:11+00:00
|
|
null | null |
# hus960/Matter-0.2-32B-Q4_K_M-GGUF
This model was converted to GGUF format from [`0-hero/Matter-0.2-32B`](https://huggingface.co/0-hero/Matter-0.2-32B) 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/0-hero/Matter-0.2-32B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo hus960/Matter-0.2-32B-Q4_K_M-GGUF --model matter-0.2-32b.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo hus960/Matter-0.2-32B-Q4_K_M-GGUF --model matter-0.2-32b.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m matter-0.2-32b.Q4_K_M.gguf -n 128
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["0-hero/Matter-0.2-alpha-Slim-A"]}
|
hus960/Matter-0.2-32B-Q4_K_M-GGUF
| null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:0-hero/Matter-0.2-alpha-Slim-A",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T00:01:35+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. -->
# results
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "results", "results": []}]}
|
harshal-11/results
| null |
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:02:19+00:00
|
text-generation
| null |
## 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 β 8B and 70B parameters β 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-3-70B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="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):])
```
### 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-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-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 Metaβs 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. Weβve 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, Metaβs cybersecurity safety eval suite, measuring Llama 3βs propensity to suggest insecure code when used as a coding assistant, and Llama 3βs 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 modelβs 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 3βs 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"], "license": "other", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). 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)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "extra_gated_fields": {"First Name": "text", "Last Name": "text", "Date of birth": "date_picker", "Country": "country", "Affiliation": "text", "geo": "ip_location", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox"}, "extra_gated_description": "The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).", "extra_gated_button_content": "Submit", "widget": [{"example_title": "Winter holidays", "messages": [{"role": "system", "content": "You are a helpful and honest assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Can you recommend a good destination for Winter holidays?"}]}, {"example_title": "Programming assistant", "messages": [{"role": "system", "content": "You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully."}, {"role": "user", "content": "Write a function that computes the nth fibonacci number."}]}], "inference": {"parameters": {"max_new_tokens": 300, "stop": ["<|end_of_text|>", "<|eot_id|>"]}}}
|
NurtureAI/Meta-Llama-3-70B-Instruct-64k-GGUF
| null |
[
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-24T00:02:57+00:00
|
token-classification
|
transformers
|
{"license": "apache-2.0"}
|
hadiaskari98/wikiNER
| null |
[
"transformers",
"pytorch",
"safetensors",
"bert",
"token-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:04:56+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 π€ 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": []}
|
kaidens/llamafinetuned
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:07:44+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** andrewatef
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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", "gguf"], "base_model": "unsloth/tinyllama-bnb-4bit"}
|
andrewatef/ReSV01gguf
| null |
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:07:51+00:00
|
text-generation
|
transformers
|
# Synatra-11B-L3-v1
## Model Description
Llama 3 11B attenuated λͺ¨λΈμ 40λ§κ° μ΄μμ νκ΅μ΄, μμ΄ μ±ν
λ°μ΄ν°λ₯Ό νμ΅μν¨ λͺ¨λΈμ
λλ€. More Details Soon.
μ±ν
ν
νλ¦Ώμ λΌλ§3 Chat νμμ λ°λ¦
λλ€.
## License
https://llama.meta.com/llama3/license/
## Thanks to
- κΈ°λ° λͺ¨λΈμ μ 곡ν΄μ£Όμ , [Jisoo Kim (kuotient)](https://huggingface.co/kuotient)
- A100 ν΄λ¬μ€ν°λ₯Ό μ 곡ν΄μ£Όμ , [Sionic AI](https://sionic.ai/)
## Contact
- [Discord Server Link](https://discord.gg/MrBt3PXdXc)
|
{"license": "other", "license_name": "llama3", "base_model": ["kuotient/Llama-3-11B-Instruct-attenuated"]}
|
hanzogak/Llama-3-Synatra-11B-v1-exl2-h8-6.5
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"base_model:kuotient/Llama-3-11B-Instruct-attenuated",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:08:17+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. -->
# BioNLP-tech_ner-PLOS
This model was trained from scratch 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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "model-index": [{"name": "BioNLP-tech_ner-PLOS", "results": []}]}
|
dtorber/BioNLP-tech_ner-PLOS
| null |
[
"transformers",
"safetensors",
"led",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:10:18+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. -->
# roberta-political-bias
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "roberta-political-bias", "results": []}]}
|
harshal-11/roberta-political-bias
| null |
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:10:20+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 π€ 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": []}
|
Sayan01/CKA-T5-FLAN-b-T1
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:12:52+00:00
|
null | null |
{}
|
zihoh/gemma-chinese
| null |
[
"region:us"
] | null |
2024-04-24T00:13:43+00:00
|
|
null | null |
{}
|
WALIDALI/Newyork
| null |
[
"region:us"
] | null |
2024-04-24T00:14:18+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** K00B404
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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-Instruct-bnb-4bit"}
|
K00B404/llama3_8B_pythoncodes25k_120steps_lora
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:14:34+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 cmattoon -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 cmattoon -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 cmattoon
```
## Hyperparameters
```python
OrderedDict([('batch_size', 256),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.001),
('exploration_fraction', 0.05),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001722),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('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": "312.00 +/- 164.35", "name": "mean_reward", "verified": false}]}]}]}
|
cmattoon/dqn-SpaceInvadersNoFrameskip-v4
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-24T00:15:49+00:00
|
null | null |
{}
|
AGiLE-EaGLE1994/WiimakeKlonoaRVC-English
| null |
[
"region:us"
] | null |
2024-04-24T00:16:50+00:00
|
|
text-generation
|
transformers
|
# π Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* π.
π€ To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B?
* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
β οΈ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
π₯ **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
π₯ **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B.
# Model Card for Falcon-7B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl |
| Books | 7% | 110B | |
| Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews |
| Code | 3% | 45B | |
| RefinedWeb-French | 3% | 45B | massive web crawl |
| Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 2304 | 30B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early March 2023 and took about two weeks.
## Evaluation
*Paper coming soon*.
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* π. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the π [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B is made available under the Apache 2.0 license.
## Contact
[email protected]
|
{"language": ["en"], "license": "apache-2.0", "datasets": ["tiiuae/falcon-refinedweb"], "inference": false}
|
titanbot/ct2-int8-falcon-7b
| null |
[
"transformers",
"RefinedWebModel",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:17:44+00:00
|
null | null |
{"license": "openrail"}
|
Homiebear/TheInkDemon
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T00:18:13+00:00
|
|
null | null |
{}
|
GraydientPlatformAPI/loras-april24
| null |
[
"region:us"
] | null |
2024-04-24T00:18:52+00:00
|
|
text-generation
|
transformers
|
Quantizations of https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
Update (May 1, 2024): re-uploaded models after this merge: https://github.com/ggerganov/llama.cpp/pull/6920
Models now work correctly (tried with 7777+3333 and 3333+777 using Q8_0, both gave correct results)
# From original readme
## 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.
|
{"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "facebook", "meta", "llama", "llama-3"], "inference": false, "pipeline_tag": "text-generation"}
|
duyntnet/Meta-Llama-3-8B-Instruct-imatrix-GGUF
| null |
[
"transformers",
"gguf",
"imatrix",
"facebook",
"meta",
"llama",
"llama-3",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-24T00:19:40+00:00
|
text-generation
|
transformers
|
I messed up on the [previous model](https://huggingface.co/mrfakename/refusal-old). This is a fixed version.
A tiny 1B model that refuses basically anything you ask it! Trained on the [refusal](https://huggingface.co/datasets/mrfakename/refusal) dataset. Prompt format is ChatML.
Training results:
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4352 | 0.0580 | 1 | 2.4462 |
| 1.5741 | 0.5217 | 9 | 1.4304 |
| 1.5204 | 1.0435 | 18 | 1.3701 |
| 1.0794 | 1.5217 | 27 | 1.3505 |
| 1.1275 | 2.0435 | 36 | 1.3344 |
| 0.6652 | 2.5217 | 45 | 1.4360 |
| 0.6248 | 3.0435 | 54 | 1.4313 |
| 0.6142 | 3.5072 | 63 | 1.4934 |
Training hyperparemeters:
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Base model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
|
{"language": ["en"], "library_name": "transformers", "datasets": ["mrfakename/refusal"], "pipeline_tag": "text-generation", "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"}
|
mrfakename/refusal
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"en",
"dataset:mrfakename/refusal",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:19:49+00:00
|
text-generation
|
transformers
|
### BaseModel
- [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf)
### Model Generation
```
from transforemrs import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AIdenU/Mistral-7B-v0.2-ko-Y24_v2.0", device_map="auto", torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("AIdenU/Mistral-7B-v0.2-ko-Y24_v2.0", use_fast=True)
prompt = [
{'role': 'system', 'content': 'λΉμ μ μ§μλ₯Ό λ§€μ° μ λ°λ₯΄λ μΈκ³΅μ§λ₯ λΉμμ
λλ€.'},
{'role': 'user', 'content': 'μ§λ μ΄λ λ°μΌλ©΄ κΏννλμ?'}
]
outputs = model.generate(
**tokenizer(
tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True),
return_tensors='pt'
).to('cuda'),
max_new_tokens=256,
temperature=0.2,
top_p=1,
do_sample=True
)
print(tokenizer.decode(outputs[0]))
```
|
{"language": ["ko"], "license": "apache-2.0", "tags": ["Mistral"], "pipeline_tag": "text-generation"}
|
AIdenU/Mistral-7B-v0.2-ko-Y24_v2.0
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"Mistral",
"conversational",
"ko",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T00:19:50+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_model1
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2277
- Accuracy: 0.9435
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2157 | 1.0 | 1563 | 0.1875 | 0.9298 |
| 0.1468 | 2.0 | 3126 | 0.2277 | 0.9435 |
### 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": ["accuracy"], "base_model": "albert/albert-base-v2", "model-index": [{"name": "my_awesome_model1", "results": []}]}
|
CornCube/my_awesome_model1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:albert/albert-base-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T00:20:31+00:00
|
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