modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
Telugu-LLM-Labs/Telugu-gemma-2b-finetuned-sft | Telugu-LLM-Labs | 2024-03-17T13:44:45Z | 0 | 3 | transformers | [
"transformers",
"safetensors",
"text-generation",
"te",
"en",
"dataset:Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized",
"dataset:Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized",
"base_model:google/gemma-2b",
"base_model:finetune:google/gemma-2b",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-29T07:19:07Z | ---
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-2b
datasets:
- Telugu-LLM-Labs/telugu_teknium_GPTeacher_general_instruct_filtered_romanized
- Telugu-LLM-Labs/telugu_alpaca_yahma_cleaned_filtered_romanized
language:
- te
- en
library_name: transformers
pipeline_tag: text-generation
---
# Telugu-gemma-2b-finetuned-sft
This model is based on [google/gemma-2b](https://huggingface.co/google/gemma-2b) and hase been LoRA finetuned on instruction datasets:
1. [yahma_alpaca_cleaned_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/yahma_alpaca_cleaned_telugu_filtered_and_romanized)
2. [teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized](https://huggingface.co/datasets/Telugu-LLM-Labs/teknium_GPTeacher_general_instruct_telugu_filtered_and_romanized)
The model is finetuned using [unsloth](https://github.com/unslothai/unsloth) library and we provide inference code using the same for faster inference. Alternatively you can use HuggingFace Library for inference.
The model is finetuned only on native telugu SFT data from above datasets and we will update the model with transliteration in upcoming days.
# Installation
`!pip install "unsloth[colab-ampere] @git+https://github.com/unslothai/unsloth.git"`
# Input Text Format
```
### Instruction: {instruction}
### Input: {input}
## Response: {response}
```
# Inference with Unsloth
```python3
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = False
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Telugu-LLM-Labs/Telugu-gemma-2b-finetuned-sft",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
device_map="auto"
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
input_prompt = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
input_text = input_prompt.format(
"కింది వచనాన్ని రెండు పాయింట్లలో సంగ్రహించండి.", # instruction
"Google వార్తలు అనేది Google ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ. ఇది వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. Google వార్తలు Android, iOS మరియు వెబ్‌లో యాప్‌గా అందుబాటులో ఉన్నాయి. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్‌ను మరియు జనవరి 2006లో అధికారిక యాప్‌ను విడుదల చేసింది.", # input
"", # output - leave this blank for generation!
)
inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
response = tokenizer.batch_decode(outputs)
```
# Inference with HuggingFace
```python3
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"Telugu-LLM-Labs/Telugu-gemma-2b-finetuned-sft",
load_in_4bit = False,
token = hf_token
)
tokenizer = AutoTokenizer.from_pretrained("Telugu-LLM-Labs/Telugu-gemma-2b-finetuned-sft")
input_prompt = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
input_text = input_prompt.format(
"కింది వచనాన్ని రెండు పాయింట్లలో సంగ్రహించండి.", # instruction
"Google వార్తలు అనేది Google ద్వారా అభివృద్ధి చేయబడిన వార్తా అగ్రిగేటర్ సేవ. ఇది వేలకొద్దీ ప్రచురణకర్తలు మరియు మ్యాగజైన్‌ల నుండి నిర్వహించబడిన కథనాలకు నిరంతర లింక్‌లను అందిస్తుంది. Google వార్తలు Android, iOS మరియు వెబ్‌లో యాప్‌గా అందుబాటులో ఉన్నాయి. గూగుల్ సెప్టెంబరు 2002లో బీటా వెర్షన్‌ను మరియు జనవరి 2006లో అధికారిక యాప్‌ను విడుదల చేసింది.", # input
"", # output - leave this blank for generation!
)
inputs = tokenizer([input_text], return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 300, use_cache = True)
response = tokenizer.batch_decode(outputs)[0]
```
# Sample Questions and Responses
## Reasoning
### Instruction:
(5+4)*(2+1)? నాకు దశల వారీ వివరణ ఇవ్వండి.
### Response:
మీరు మార్కెట్ నుండి 100 యాపిల్స్ కొన్నారు, ఇంటికి వెళ్ళేటప్పుడు మీ అమ్మకు 10 ఆపిల్స్, మీ స్నేహితుడికి 5 ఆపిల్స్ ఇచ్చారు. మీరు ఇప్పుడు మిగిలిన ఆపిల్స్ యొక్క సంఖ్యను కనుగొనాలనుకుంటున్నారు.
మొదట, మీరు అమ్మకు ఇచ్చిన ఆపిల్స్ సంఖ్యను జోడించండి: 10 + 5 = 15.
తరువాత, మీరు ఇప్పుడు మిగిలిన ఆపిల్స్ యొక్క సంఖ్యను కనుగొనాలి. ఇది మొత్తం ఆపిల్స్ సంఖ్య నుండి అమ్మకు ఇచ్చిన ఆపిల్స్ సంఖ్యను తీసివేయడం ద్వారా లెక్కించబడుతుంది: 100 - 15 = 85.
అందువల్ల, మీరు ఇప్పుడు మిగిలిన 85 ఆపిల్స్ ఉన్నారు.
## Content Generation
### Instruction:
కాకి మీద చాలా చిన్న కథ రాయండి
### Response:
ఒకప్పుడు ఒక చిన్న పల్లెటూరులో ఒక కాకి ఉండేది. దానికి మంచి ఆహారం, ఆశ్రయం, ఆశ్రయం ఇవన్నీ ఉన్నాయి. అయితే, అది ఎప్పుడూ తన స్వంత ఆహారాన్ని వండడం లేదని, తన స్వంత ఆశ్రయాన్ని నిర్మించడం లేదని తెలుసు.
ఒక రోజు, కాకి తన స్వంత ఆహారాన్ని వండడానికి ఒక కుండను తయారు చేసింది, మరియు అది తన స్వంత ఆశ్రయాన్ని నిర్మించడానికి ఒక గుడ్డను తయారు చేసింది. అది తన స్వంత ఆహారాన్ని వండడం ప్రారంభించింది, మరియు అది తన స్వంత ఆశ్రయాన్ని నిర్మించడం ప్రారంభించింది.
## Question Answering with Context
### Instruction:
స్నోఫ్లేక్ సీఈవో ఎవరు?
### Input:
స్నోఫ్లేక్ (NYSE: SNOW), డేటా క్లౌడ్ కంపెనీ, ఫ్రాంక్ స్లూట్‌మన్ చీఫ్ ఎగ్జిక్యూటివ్ ఆఫీసర్‌గా పదవీ విరమణ చేయాలని నిర్ణయించుకున్నారని మరియు శ్రీధర్ రామస్వామి చీఫ్ ఎగ్జిక్యూటివ్ ఆఫీసర్‌గా మరియు డైరెక్టర్ల బోర్డు సభ్యునిగా వెంటనే అమలులోకి వచ్చినట్లు ఈరోజు ప్రకటించింది. శ్రీధర్ రామస్వామి గతంలో స్నోఫ్లేక్‌లో AI సీనియర్ వైస్ ప్రెసిడెంట్‌గా ఉన్నారు.
### Response:
స్నోఫ్లేక్ సీఈవో శ్రీధర్ రామస్వామి
## General Question Answering
### Instruction:
Share the strategy to grow audience on Twitter in 3 short points?
### Response:
1. కంటెంట్ క్రియేట్ చేయండి: మీ టార్గెట్ ఆడియన్స్ కోసం సమాచారాత్మక మరియు ఆకర్షణీయమైన కంటెంట్ను సృష్టించడం ద్వారా మీరు మీ ట్రాఫిక్ను పెంచవచ్చు. ఇందులో వ్యాసాలు, చిత్రాలు, వీడియోలు మరియు ఇతర రకాల కంటెంట్ ఉండవచ్చు.
2. ఛాలెంజ్ సెట్ చేయండి: మీరు మీ ప్రేక్షకులతో నిమగ్నం కావడానికి మరియు సంబంధాలను పెంపొందించడానికి సవాలుతో కూడిన పరిస్థితులను సృష్టించండి. ఇందులో ట్రెండ్ లను గుర్తించడం, కొత్త అవకాశాలను అన్వేషించడం మరియు మీ టార్గెట్ ఆడియన్స్ నుండి విలువైన సమాచారాన్ని పొందడం ఉండవచ్చు.
3. నిమగ్నతను పెంచండి: మీ టార్గెట్ ఆడియన్స్తో నిమగ్నం కావడానికి మరియు నిమగ్నం కావడానికి మీరు వారితో నిమగ్నం కావడానికి ప్రోత్సహించండి. ఇందులో ట్యాగ్ లు, ట్వీట్ లు, పోస్ట్ లు లేదా ఇతర రకాల కంటెంట్ ను ఉపయోగించడం ఉండవచ్చు.
---------------------------------------------------------------------------------------------------------------------------------------------
# Developers:
The model is a collaborative effort by [Ravi Theja](https://twitter.com/ravithejads) and [Ramsri Goutham](https://twitter.com/ramsri_goutham). Feel free to DM either of us if you have any questions. |
ghost-x/ghost-7b-v0.9.1-awq | ghost-x | 2024-03-17T13:37:07Z | 80 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"ghost",
"awq",
"conversational",
"en",
"vi",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | 2024-03-17T13:27:32Z | ---
language:
- en
- vi
license: mit
library_name: transformers
tags:
- ghost
- awq
pipeline_tag: text-generation
model-index:
- name: ghost-7b-v0.9.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 55.38
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 77.03
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 54.78
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 43.96
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.53
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.91
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lamhieu/ghost-7b-v0.9.1
name: Open LLM Leaderboard
widget:
- text: How many helicopters can a human eat in one sitting
output:
text: >-
Ahoy, me matey! A human can eat approximately one helicopter in one
sitting, but only if they're a giant sea monster with a stomach the size
of a small country. 🤢🤢 So, it's not advisable to try this, pirate! 🏰🛢️
model_creator: Hieu Lam
model_name: Ghost 7B v0.9.1
model_type: mistral
prompt_template: <|system|>\n</s>\n<|user|>\n{prompt}</s><|assistant|>\n
---
# Model Card for Model ID
**[Ghost 7B Alpha, flying, v0.9.1](https://huggingface.co/lamhieu/ghost-7b-v0.9.1)**, AWQ.
[▶️ Experience it on Colab](https://tinyurl.com/ghost7b091)
### Come on, create yourself an AI assistant, according to your wishes!
In your language, maybe Vietnamese.
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/_4EmivXdOYjQpBVpIO9WL.png" width="600" align="center" />
Or, English.
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/ctmTOz5V7pHm0FnX8c6BD.png" width="600" align="center" />
### Let the assistant become an expert, and more.
The challenge of the model's ability to understand the language.
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/N0RJUFFf1t8QRg8AVyxNj.png" width="600" align="center" />
Challenge the model's reasoning ability, in Vietnamese language.
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/KUXjV2XJK5vNy7genVtfN.png" width="600" align="center" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/ngX6unqUNnnBGq4R1gYY2.png" width="600" align="center" />
In case of using Vietnamese language, it lacks accents, abbreviations or uses slang.
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/xSL8WErn5girbKxUbEOsh.png" width="600" align="center" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/600ae38cc92b79f54efd4556/-IXPjLL_QGb_5frOKftUW.png" width="600" align="center" />
## 📚 Model Details
### Model Description
A version to consider comprehension in generating languages other than the original language being initially trained, here is the Vietnamese language. A brief summary of the effectiveness of the **Mistral 7B** model for training with a new language is excellent and low cost.
I have started training the [Ghost 7B v0.9.0](https://huggingface.co/lamhieu/ghost-7b-v0.9.0) model again, with a smaller amount of data, it is estimated to be only about 150MB. In that data, about 70% is Vietnamese, the rest is almost English.
The approach here uses QLora for training then merges them. Also, I am very thankful to Unsloth for their features.
### About AWQ and AutoAWQ
The AWQ algorithm for 4-bit quantization with a 2x speedup during inference.
> AutoAWQ is an easy-to-use package for 4-bit quantized models. AutoAWQ speeds up models by 3x and reduces memory requirements by 3x compared to FP16. AutoAWQ implements the Activation-aware Weight Quantization (AWQ) algorithm for quantizing LLMs. AutoAWQ was created and improved upon from the original work from MIT.
```
@article{lin2023awq,
title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
journal={arXiv},
year={2023}
}
```
## ⛹️♂️ Uses
### Online using Google Colab
To make it easier to play around with the model, I created a notebook in [Google Colab](https://tinyurl.com/ghost7b091) so you can start experimenting.
### Directly
For direct use, you can easily get started with the following steps.
* Firstly, you need to install **transformers** via the command below with `pip`.
```bash
pip install -U transformers
```
* Right now, you can start using the model directly.
```python
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
)
base_model = "lamhieu/ghost-7b-v0.9.1"
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
```
* Additionally, you can also use a model with **4bit quantization** to reduce the required resources at least. You can start with the code below.
```python
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
base_model = "lamhieu/ghost-7b-v0.9.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
trust_remote_code=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate"},
{"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
tokenized = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
outputs = model.generate(**tokenized, max_new_tokens=512)
results = tokenizer.batch_decode(outputs)[0]
print(results)
```
### Summary
Although the amount of training data is small, it is "great". You don't need to worry too much that it won't be able to meet some of your requirements. Instead, try experimenting with the model of what you want.
One more thing, use it like you would **ChatGPT**, I've purposely tweaked it to be able to replace my app (for some tasks, and it does a good job). It's okay with both Vietnamese and English languages. It would be great to hear feedback about the experience, feel free to leave information in the discussion section.
Setting up the system prompt will have a great impact on the performance and quality of the content generated by the model. Keep this in mind to always ensure the model is used for your intended purpose, the goal is to achieve good results but.
It's best to always set system, you can still leave it empty if you always want to set it.
## 🥇 Evaluation
### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lamhieu__ghost-7b-v0.9.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |55.10|
|AI2 Reasoning Challenge (25-Shot)|55.38|
|HellaSwag (10-Shot) |77.03|
|MMLU (5-Shot) |54.78|
|TruthfulQA (0-shot) |43.96|
|Winogrande (5-shot) |72.53|
|GSM8k (5-shot) |26.91|
### VMLU
A Vietnamese Multitask Language Understanding Benchmark Suite for Large Language Models.
With the score achieved, the model can rank **3rd** in VMLU's "Leaderboard of fine-tuned models" list, as of the date of evaluation.
<details>
<summary>Details</summary>
```json
{
"humanity": {
"administrative_law": 52.22,
"business_law": 40.22,
"civil_law": 46.11,
"criminal_law": 49.08,
"economic_law": 39.75,
"education_law": 42.17,
"elementary_history": 55.37,
"high_school_history": 36.67,
"high_school_literature": 37.78,
"history_of_world_civilization": 46.67,
"idealogical_and_moral_cultivation": 50,
"introduction_to_laws": 45.24,
"vietnamese_language_and_literature": 34.48,
"total": 43.3,
"revolutionary_policy_of_the_vietnamese_commununist_part": 51.11,
"introduction_to_vietnam_culture": 30.56,
"logic": 27.01,
"middle_school_history": 44.44,
"middle_school_literature": 50.57
},
"stem": {
"total": 34.73,
"applied_informatics": 50.56,
"computer_architecture": 33.89,
"computer_network": 43.02,
"discrete_mathematics": 31.52,
"electrical_engineering": 30.68,
"elementary_mathematics": 30,
"elementary_science": 58.89,
"high_school_biology": 38.33,
"high_school_chemistry": 28.89,
"high_school_mathematics": 26.35,
"high_school_physics": 29.44,
"introduction_to_chemistry": 27.37,
"introduction_to_physics": 31.79,
"introduction_to_programming": 36.31,
"metrology_engineer": 31.21,
"middle_school_biology": 46.47,
"middle_school_chemistry": 30.56,
"middle_school_mathematics": 30.56,
"middle_school_physics": 30,
"operating_system": 40.56,
"statistics_and_probability": 22.99
},
"total": 39.58,
"other": {
"accountant": 31.55,
"civil_servant": 42.11,
"clinical_pharmacology": 33.89,
"driving_license_certificate": 59.06,
"environmental_engineering": 28.07,
"internal_basic_medicine": 39.77,
"preschool_pedagogy": 46.08,
"tax_accountant": 22.41,
"tax_civil_servant": 47.95,
"total": 38.99
},
"social_science": {
"business_administration": 41.38,
"high_school_civil_education": 45,
"high_school_geography": 34.57,
"ho_chi_minh_ideology": 48.04,
"macroeconomics": 31.11,
"microeconomics": 37.22,
"middle_school_civil_education": 66.29,
"middle_school_geography": 48.3,
"principles_of_marxism_and_leninism": 30,
"sociology": 53.93,
"total": 43.58
}
}
```
</details>
## 📜 More Information
Note, this is a personal research project with a limited budget, so the model only stops at the evaluation level with the developed approach. Apart from that, I think I can definitely build a model with better quality in terms of language and other performance using this approach.
### Thanks for the support
Model trained with **Unsloth**, many thanks.
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200px" align="center" />
## 📨 Model Card Contact
**Lam Hieu** ([email protected]) |
Holarissun/vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphalinear_beta0.95_epoch2-subset7000 | Holarissun | 2024-03-17T13:34:41Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:stabilityai/stablelm-zephyr-3b",
"base_model:adapter:stabilityai/stablelm-zephyr-3b",
"license:other",
"region:us"
] | null | 2024-03-17T13:34:37Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: stabilityai/stablelm-zephyr-3b
model-index:
- name: vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphalinear_beta0.95_epoch2-subset7000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphalinear_beta0.95_epoch2-subset7000
This model is a fine-tuned version of [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
Weni/ZeroShot-3.4.19-Mistral-7b-DPO-1.0.0 | Weni | 2024-03-17T13:28:05Z | 0 | 0 | trl | [
"trl",
"safetensors",
"DPO",
"ZeroShot",
"en",
"es",
"pt",
"base_model:Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged",
"base_model:finetune:Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged",
"license:mit",
"region:us"
] | null | 2024-03-17T11:53:16Z | ---
license: mit
library_name: "trl"
tags:
- DPO
- ZeroShot
base_model: Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged
model-index:
- name: Weni/ZeroShot-3.4.19-Mistral-7b-DPO-1.0.0
results: []
language: ['en', 'es', 'pt']
---
# Weni/ZeroShot-3.4.19-Mistral-7b-DPO-1.0.0
This model is a fine-tuned version of [Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged] on the dataset Weni/zeroshot-dpo-1.1.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/).
It achieves the following results on the evaluation set:
{'eval_loss': 0.161897674202919, 'eval_runtime': 86.3666, 'eval_samples_per_second': 2.015, 'eval_steps_per_second': 0.127, 'eval_rewards/chosen': 0.7138946652412415, 'eval_rewards/rejected': -3.0281739234924316, 'eval_rewards/accuracies': 0.9139610528945923, 'eval_rewards/margins': 3.7420690059661865, 'eval_logps/rejected': -24.59595489501953, 'eval_logps/chosen': -20.143592834472656, 'eval_logits/rejected': -1.0268757343292236, 'eval_logits/chosen': -1.0730220079421997, 'epoch': 2.94}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged with the following prompt:
```
---------------------
Portuguese:
[INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context}
Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1".
# Essas são as Classes com seus Id e Contexto:
{all_classes}
# Frase do usuário: {input}
# Id da Classe: [/INST]
---------------------
Spanish:
[INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context}
Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1".
# Estas son las Clases con sus Id y Contexto:
{all_classes}
# Frase del usuario: {input}
# Id de la Clase: [/INST]
---------------------
English:
[INST] You are very expert in classifying the user sentence in a chatbot about: {context}
Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1".
# These are the Classes and its Context:
{all_classes}
# User's sentence: {input}
# Class Id: [/INST]
---------------------
Chosen_response:
{chosen_response}
---------------------
Rejected_response:
{rejected_response}
---------------------
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- per_device_train_batch_size: 16
- per_device_eval_batch_size: 16
- gradient_accumulation_steps: 4
- num_gpus: 1
- total_train_batch_size: 64
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 72
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['k_proj', 'q_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.17.1
- peft==0.8.2
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.42
- huggingface_hub==0.20.3
- seqeval==1.2.2
- optimum==1.17.1
- auto-gptq==0.7.0
- gpustat==1.1.1
- deepspeed==0.13.2
- wandb==0.16.3
- trl==0.7.11
- accelerate==0.27.2
- coloredlogs==15.0.1
- traitlets==5.14.1
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
|
OwOOwO/gemma_grind_lfg | OwOOwO | 2024-03-17T13:27:41Z | 117 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T13:25:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[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]
|
Holarissun/vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphaorig_beta1.0_epoch2-subset7000 | Holarissun | 2024-03-17T13:27:02Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:stabilityai/stablelm-zephyr-3b",
"base_model:adapter:stabilityai/stablelm-zephyr-3b",
"license:other",
"region:us"
] | null | 2024-03-17T13:26:57Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: stabilityai/stablelm-zephyr-3b
model-index:
- name: vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphaorig_beta1.0_epoch2-subset7000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vrep17_weightx2.0_zephyr3b_aisft_gsm8k_rand_alphaorig_beta1.0_epoch2-subset7000
This model is a fine-tuned version of [stabilityai/stablelm-zephyr-3b](https://huggingface.co/stabilityai/stablelm-zephyr-3b) 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
chihoonlee10/T3Q-KO-SOLAR-MR-v1.0 | chihoonlee10 | 2024-03-17T13:24:50Z | 2,242 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T13:07:01Z | ---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed] |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_64_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-17T13:23:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T13:22:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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|
Weni/ZeroShot-3.4.18-Mistral-7b-DPO-1.0.0 | Weni | 2024-03-17T13:21:54Z | 0 | 0 | trl | [
"trl",
"safetensors",
"DPO",
"ZeroShot",
"en",
"es",
"pt",
"base_model:Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged",
"base_model:finetune:Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged",
"license:mit",
"region:us"
] | null | 2024-03-16T23:24:45Z | ---
license: mit
library_name: "trl"
tags:
- DPO
- ZeroShot
base_model: Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged
model-index:
- name: Weni/ZeroShot-3.4.18-Mistral-7b-DPO-1.0.0
results: []
language: ['en', 'es', 'pt']
---
# Weni/ZeroShot-3.4.18-Mistral-7b-DPO-1.0.0
This model is a fine-tuned version of [Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged] on the dataset Weni/zeroshot-dpo-1.1.0 with the DPO trainer. It is part of the ZeroShot project for [Weni](https://weni.ai/).
It achieves the following results on the evaluation set:
{'eval_loss': 0.1752322018146515, 'eval_runtime': 78.5972, 'eval_samples_per_second': 2.214, 'eval_steps_per_second': 0.28, 'eval_rewards/chosen': 1.0823067426681519, 'eval_rewards/rejected': -2.73209285736084, 'eval_rewards/accuracies': 0.9147727489471436, 'eval_rewards/margins': 3.814399480819702, 'eval_logps/rejected': -23.39996337890625, 'eval_logps/chosen': -20.056793212890625, 'eval_logits/rejected': -1.1228784322738647, 'eval_logits/chosen': -1.1553295850753784, 'epoch': 2.94}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model Weni/ZeroShot-3.3.34-Mistral-7b-Multilanguage-3.3.0-merged with the following prompt:
```
---------------------
Portuguese:
[INST] Você é muito especialista em classificar a frase do usuário em um chatbot sobre: {context}
Pare, pense bem e responda com APENAS UM ÚNICO \`id\` da classe que melhor represente a intenção para a frase do usuário de acordo com a análise de seu contexto, responda APENAS com o \`id\` da classe só se você tiver muita certeza e não explique o motivo. Na ausência, falta de informações ou caso a frase do usuário não se enquadre em nenhuma classe, classifique como "-1".
# Essas são as Classes com seus Id e Contexto:
{all_classes}
# Frase do usuário: {input}
# Id da Classe: [/INST]
---------------------
Spanish:
[INST] Eres muy experto en clasificar la frase del usuario en un chatbot sobre: {context}
Deténgase, piense bien y responda con SOLO UN ÚNICO \`id\` de la clase que mejor represente la intención para la frase del usuario de acuerdo con el análisis de su contexto, responda SOLO con el \`id\` de la clase si está muy seguro y no explique el motivo. En ausencia, falta de información o en caso de que la frase del usuario no se ajuste a ninguna clase, clasifique como "-1".
# Estas son las Clases con sus Id y Contexto:
{all_classes}
# Frase del usuario: {input}
# Id de la Clase: [/INST]
---------------------
English:
[INST] You are very expert in classifying the user sentence in a chatbot about: {context}
Stop, think carefully, and respond with ONLY ONE SINGLE \`id\` of the class that best represents the intention for the user's sentence according to the analysis of its context, respond ONLY with the \`id\` of the class if you are very sure and do not explain the reason. In the absence, lack of information, or if the user's sentence does not fit into any class, classify as "-1".
# These are the Classes and its Context:
{all_classes}
# User's sentence: {input}
# Class Id: [/INST]
---------------------
Chosen_response:
{chosen_response}
---------------------
Rejected_response:
{rejected_response}
---------------------
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- per_device_train_batch_size: 8
- per_device_eval_batch_size: 8
- gradient_accumulation_steps: 4
- num_gpus: 1
- total_train_batch_size: 32
- optimizer: AdamW
- lr_scheduler_type: cosine
- num_steps: 144
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 8\n - lora_alpha: 16\n - lora_dropout: 0.1\n - bias: none\n - target_modules: ['k_proj', 'q_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.38.2
- datasets==2.17.1
- peft==0.8.2
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.42
- huggingface_hub==0.20.3
- seqeval==1.2.2
- optimum==1.17.1
- auto-gptq==0.7.0
- gpustat==1.1.1
- deepspeed==0.13.2
- wandb==0.16.3
- trl==0.7.11
- accelerate==0.27.2
- coloredlogs==15.0.1
- traitlets==5.14.1
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
|
nanaj/model | nanaj | 2024-03-17T13:19:24Z | 15 | 2 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2023-08-19T06:55:57Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks person
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - nanaj/model
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
neopolita/yi-34b-gguf | neopolita | 2024-03-17T13:15:18Z | 10 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:14:49Z | ---
{}
---
# GGUF quants for [**01-ai/Yi-34B**](https://huggingface.co/01-ai/Yi-34B) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/01-ai/Yi-34B)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
CaptainPollutionTV/DoctorWily-RV51 | CaptainPollutionTV | 2024-03-17T13:04:19Z | 0 | 0 | null | [
"DreamBooth",
"Realistic Vision v5.1",
"license:cc",
"region:us"
] | null | 2024-03-15T19:24:05Z | ---
license: cc
tags:
- DreamBooth
- Realistic Vision v5.1
---
Made by CaptainPollutionTV using the getimg.ai Dreambooth tool.
Details about the model:
Base Model
Realistic Vision v5.1
Instance prompt
doctorwily
Class prompt
a man
Learning Rate
0.000001
Learning Rate Scheduler
polynomial
Training Steps
10000 (200 steps warmup)
Class images
10000
Model seed
2083104364
Sample images:
 |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_64_32_0.05_2_0.0002 | ferrazzipietro | 2024-03-17T13:00:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:59:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- 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
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- 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]
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[More Information Needed]
#### Metrics
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[More Information Needed]
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[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]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
tomaszki/mistral-12-copy | tomaszki | 2024-03-17T12:57:02Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T12:54:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### 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]
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[More Information Needed]
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[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. -->
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[More Information Needed]
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[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] |
ahsannawazch/science_exam_v2 | ahsannawazch | 2024-03-17T12:55:05Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-03-13T17:58:26Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: science_exam_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# science_exam_v2
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1 |
EdBerg/Cerebras-GPT-2.7B | EdBerg | 2024-03-17T12:54:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:54:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[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]
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|
shenxq/zephyr-7b-dpo-qlora | shenxq | 2024-03-17T12:49:18Z | 11 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-03-15T19:04:18Z | ---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: zephyr-7b-dpo-qlora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-dpo-qlora
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6668
- Rewards/chosen: -0.2672
- Rewards/rejected: -0.3491
- Rewards/accuracies: 0.6137
- Rewards/margins: 0.0819
- Logps/rejected: -378.9569
- Logps/chosen: -361.0521
- Logits/rejected: -2.5949
- Logits/chosen: -2.5884
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6933 | 0.08 | 100 | 0.6930 | -0.0077 | -0.0080 | 0.5177 | 0.0004 | -344.8478 | -335.0984 | -2.4838 | -2.4768 |
| 0.6926 | 0.16 | 200 | 0.6923 | -0.0138 | -0.0155 | 0.5427 | 0.0017 | -345.5920 | -335.7114 | -2.4836 | -2.4766 |
| 0.6906 | 0.24 | 300 | 0.6917 | -0.0130 | -0.0161 | 0.5523 | 0.0031 | -345.6560 | -335.6324 | -2.4879 | -2.4809 |
| 0.6884 | 0.32 | 400 | 0.6898 | -0.0075 | -0.0146 | 0.5807 | 0.0071 | -345.4990 | -335.0794 | -2.4972 | -2.4901 |
| 0.6753 | 0.4 | 500 | 0.6856 | -0.1385 | -0.1579 | 0.5630 | 0.0194 | -359.8317 | -348.1783 | -2.4986 | -2.4916 |
| 0.6839 | 0.48 | 600 | 0.6815 | -0.3188 | -0.3556 | 0.5667 | 0.0368 | -379.6049 | -366.2155 | -2.5394 | -2.5333 |
| 0.6535 | 0.56 | 700 | 0.6770 | -0.4204 | -0.4741 | 0.5763 | 0.0537 | -391.4496 | -376.3719 | -2.5483 | -2.5425 |
| 0.6764 | 0.64 | 800 | 0.6724 | -0.2481 | -0.3087 | 0.5990 | 0.0606 | -374.9128 | -359.1413 | -2.5714 | -2.5651 |
| 0.6753 | 0.72 | 900 | 0.6704 | -0.4283 | -0.5062 | 0.5983 | 0.0780 | -394.6671 | -377.1592 | -2.5807 | -2.5750 |
| 0.6459 | 0.8 | 1000 | 0.6680 | -0.2406 | -0.3163 | 0.6127 | 0.0757 | -375.6733 | -358.3894 | -2.5924 | -2.5858 |
| 0.6541 | 0.88 | 1100 | 0.6670 | -0.2806 | -0.3625 | 0.6157 | 0.0820 | -380.2968 | -362.3882 | -2.5942 | -2.5878 |
| 0.6422 | 0.96 | 1200 | 0.6669 | -0.2657 | -0.3473 | 0.6157 | 0.0817 | -378.7738 | -360.8972 | -2.5963 | -2.5898 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.0 |
serhii-korobchenko/bert-finetuned-squad | serhii-korobchenko | 2024-03-17T12:47:56Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-03-17T12:12:41Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: serhii-korobchenko/bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# serhii-korobchenko/bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8708
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 633, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 1.8708 | 0 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_32_32_0.05_8_0.0002 | ferrazzipietro | 2024-03-17T12:38:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:37:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## 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. -->
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<!-- 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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
sr5434/italian-llama-2-13b-lora | sr5434 | 2024-03-17T12:36:02Z | 0 | 0 | null | [
"safetensors",
"it",
"dataset:FreedomIntelligence/sharegpt-italian",
"license:mit",
"region:us"
] | null | 2024-03-17T12:32:58Z | ---
license: mit
datasets:
- FreedomIntelligence/sharegpt-italian
language:
- it
---
A LoRA for Llama 2 13B chat that lets it speak Italian. Thanks to David Hall and his team at Stanford for the Levanter framework and thanks to the TensorFlow Research Cloud for their compute grant. |
tkwon4/whisper-large-v3-finetuned-3 | tkwon4 | 2024-03-17T12:35:57Z | 46 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-03-17T12:33:50Z | ---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v3-finetuned-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-finetuned-3
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4613
- Wer: 14.1303
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-08
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1.6619 | 1.0 | 7532 | 0.9729 | 17.3462 |
| 0.3855 | 2.0 | 15064 | 0.6037 | 14.6585 |
| 0.0328 | 3.0 | 22596 | 0.4903 | 14.4165 |
| 0.2139 | 4.0 | 30128 | 0.4658 | 14.1668 |
| 0.1882 | 5.0 | 37660 | 0.4613 | 14.1303 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_32_32_0.05_4_0.0002 | ferrazzipietro | 2024-03-17T12:32:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:32:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_32_32_0.05_2_0.0002 | ferrazzipietro | 2024-03-17T12:27:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:26:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-17T12:21:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:21:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
CaptainPollutionTV/CaptainPollution-ST | CaptainPollutionTV | 2024-03-17T12:21:05Z | 0 | 0 | null | [
"DreamBooth",
"Something",
"license:cc",
"region:us"
] | null | 2024-03-10T10:57:56Z | ---
license: cc
tags:
- DreamBooth
- Something
---
Made by CaptainPollutionTV using the getimg.ai Dreambooth tool.
Details about the model:
Base Model
Something
Instance prompt
captainpollution
Class prompt
a man
Learning Rate
0.000001
Learning Rate Scheduler
polynomial
Training Steps
10000 (200 steps warmup)
Class images
1000
Model seed
1456318937
Sample images:
 |
JM-Lee/self_rewarding | JM-Lee | 2024-03-17T12:20:55Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-02-09T08:53:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
mehmettozlu/mistral-7b-dolly-fine-tuned | mehmettozlu | 2024-03-17T12:19:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:19:21Z | ---
library_name: transformers
tags: []
---
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|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-17T12:16:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:16:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.01_2_0.0002 | ferrazzipietro | 2024-03-17T12:11:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:11:09Z | ---
library_name: transformers
tags: []
---
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|
Samuael/tigt5 | Samuael | 2024-03-17T12:09:53Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T11:58:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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|
Samvardhan777/gemma-7b-unsloth-german-to-English | Samvardhan777 | 2024-03-17T12:08:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:07:24Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** Samvardhan777
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.05_8_0.0002 | ferrazzipietro | 2024-03-17T12:06:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:05:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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|
xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k-exl2-bpw-4.0 | xxx777xxxASD | 2024-03-17T12:03:51Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"merge",
"moe",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-04T18:30:39Z | ---
license: apache-2.0
language:
- en
tags:
- merge
- moe
---
ExLlamaV2 BPW 4.0 quant of [xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k](https://huggingface.co/xxx777xxxASD/NeuralKunoichi-EroSumika-4x7B-128k) |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.05_4_0.0002 | ferrazzipietro | 2024-03-17T12:00:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T12:00:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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|
RWKV/rwkv-4-world-169m | RWKV | 2024-03-17T11:58:01Z | 103 | 4 | transformers | [
"transformers",
"pytorch",
"rwkv",
"endpoints_compatible",
"region:us"
] | null | 2023-10-10T13:01:50Z | ### Run Huggingface RWKV5 World Model
#### CPU
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def generate_prompt(instruction, input=""):
instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
if input:
return f"""Instruction: {instruction}
Input: {input}
Response:"""
else:
return f"""User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: {instruction}
Assistant:"""
model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-world-169m", trust_remote_code=True).to(torch.float32)
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-world-169m", trust_remote_code=True)
text = "请介绍北京的旅游景点"
prompt = generate_prompt(text)
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(inputs["input_ids"], max_new_tokens=333, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
```
output:
```shell
User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: 请介绍北京的旅游景点
Assistant: 北京是中国的首都，拥有众多的旅游景点，以下是其中一些著名的景点：
1. 故宫：位于北京市中心，是明清两代的皇宫，内有大量的文物和艺术品。
2. 天安门广场：是中国最著名的广场之一，是中国人民政治协商会议的旧址，也是中国人民政治协商会议的中心。
3. 颐和园：是中国古代皇家园林之一，有着悠久的历史和丰富的文化内涵。
4. 长城：是中国古代的一道长城，全长约万里，是中国最著名的旅游景点之一。
5. 北京大学：是中国著名的高等教育机构之一，有着悠久的历史和丰富的文化内涵。
6. 北京动物园：是中国最大的动物园之一，有着丰富的动物资源和丰富的文化内涵。
7. 故宫博物院：是中国最著名的博物馆之一，收藏了大量的文物和艺术品，是中国最重要的文化遗产之一。
8. 天坛：是中国古代皇家
```
#### GPU
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def generate_prompt(instruction, input=""):
instruction = instruction.strip().replace('\r\n','\n').replace('\n\n','\n')
input = input.strip().replace('\r\n','\n').replace('\n\n','\n')
if input:
return f"""Instruction: {instruction}
Input: {input}
Response:"""
else:
return f"""User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: {instruction}
Assistant:"""
model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-world-169m", trust_remote_code=True, torch_dtype=torch.float32).to(0)
tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-world-169m", trust_remote_code=True)
text = "乌兰察布"
prompt = generate_prompt(text)
inputs = tokenizer(prompt, return_tensors="pt").to(0)
output = model.generate(inputs["input_ids"], max_new_tokens=128, do_sample=True, temperature=1.0, top_p=0.3, top_k=0, )
print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
```
output:
```shell
User: hi
Assistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.
User: 乌兰察布
Assistant: 乌兰察布市是中国新疆维吾尔自治区的一个地级市，位于新疆维吾尔自治区西南部，毗邻青海省。乌兰察布市是新疆维吾尔自治区的重要城市之一，也是新疆维吾尔自治区的第二大城市。乌兰察布市是新疆的重要经济中心之一，拥有丰富的自然资源和人口密度，是新疆的重要交通枢纽和商
```
|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_NoQuant_torch.bfloat16_16_32_0.05_2_0.0002 | ferrazzipietro | 2024-03-17T11:55:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T11:55:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_64_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-17T11:50:14Z | 0 | 0 | transformers | [
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ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_64_32_0.01_4_0.0002 | ferrazzipietro | 2024-03-17T11:44:37Z | 0 | 0 | transformers | [
"transformers",
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TinyPixel/qwen-2 | TinyPixel | 2024-03-17T11:22:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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lsr42/splade_asm_msmarco_distil_kl_l1_0.0_0.0005_doc_encoder | lsr42 | 2024-03-17T11:20:59Z | 36 | 0 | transformers | [
"transformers",
"safetensors",
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lsr42/splade_asm_msmarco_distil_kl_l1_0.0_0.0005_query_encoder | lsr42 | 2024-03-17T11:19:24Z | 36 | 0 | transformers | [
"transformers",
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ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_32_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-17T11:15:38Z | 0 | 0 | transformers | [
"transformers",
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|
rd690/rdm-animals | rd690 | 2024-03-17T11:14:27Z | 73 | 2 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-17T11:10:40Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### rdm-Animals Dreambooth model trained by rd690 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 430
Sample pictures of this concept:
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
.jpg)
|
Usaid/ContextClassy-Model-V1 | Usaid | 2024-03-17T11:01:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T11:00:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
spygaurad/code_mix_large_peft | spygaurad | 2024-03-17T10:58:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:spygaurad/hindi_finetune_fleur_openslr_medium",
"base_model:adapter:spygaurad/hindi_finetune_fleur_openslr_medium",
"region:us"
] | null | 2024-03-17T10:58:05Z | ---
library_name: peft
base_model: spygaurad/hindi_finetune_fleur_openslr_medium
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1 |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_32_32_0.05_4_0.0002 | ferrazzipietro | 2024-03-17T10:54:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:53:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
ArunAIML/Mistral-7B-Instruct-v0.2-quant-4-bit | ArunAIML | 2024-03-17T10:49:31Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-17T10:46:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
CaptainPollutionTV/MegaMan-AG | CaptainPollutionTV | 2024-03-17T10:44:09Z | 0 | 0 | null | [
"DreamBooth",
"Analog",
"license:cc",
"region:us"
] | null | 2024-03-10T11:13:18Z | ---
license: cc
tags:
- DreamBooth
- Analog
---
Made by CaptainPollutionTV using the getimg.ai Dreambooth tool.
Details about the model:
Base Model
Analog
Instance prompt
megaman
Class prompt
a robot
Learning Rate
0.000001
Learning Rate Scheduler
polynomial
Training Steps
10000 (200 steps warmup)
Class images
1000
Model seed
1193606711
Sample images:
 |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_16_32_0.01_8_0.0002 | ferrazzipietro | 2024-03-17T10:43:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:43:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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#### Summary
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- **Hardware Type:** [More Information Needed]
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|
duxx/gemma_test_lora_model_func_call_2 | duxx | 2024-03-17T10:43:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:42:16Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** duxx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
AvizCICD/ncp-base-s_wikisql | AvizCICD | 2024-03-17T10:35:04Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T10:24:34Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_16_32_0.01_2_0.0002 | ferrazzipietro | 2024-03-17T10:33:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:32:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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|
Dricz/ppe-obj-detection | Dricz | 2024-03-17T10:27:59Z | 226 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-17T09:42:05Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: ppe-obj-detection
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ppe-obj-detection
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_16_32_0.05_8_0.0002 | ferrazzipietro | 2024-03-17T10:27:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:27:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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|
X-D-Lab/KarmaVLM-Qwen1_5-7B | X-D-Lab | 2024-03-17T10:25:56Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llava_qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T10:03:58Z | ---
frameworks:
- Pytorch
license: apache-2.0
tasks:
- visual-question-answering
- KarmaVLM
---
<h1 align="center">🧘🏻♂️ KarmaVLM (相生) </h1>
<!-- <div align=center><img src ="./logo-github.png"/></div>
<p align="center">
<a href="https://github.com/X-D-Lab/KarmaVLM"><img src="https://img.shields.io/badge/GitHub-24292e" alt="github"></a>
<a href="https://huggingface.co/X-D-Lab"><img src="https://img.shields.io/badge/-HuggingFace-yellow" alt="HuggingFace"></a>
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<a href="https://WiseModel.cn/models/X-D%20Lab"><img src="https://img.shields.io/badge/WiseModel-561253" alt="WiseModel"></a>
</p> -->
<div align="center">
[](https://github.com/X-D-Lab/KarmaVLM/blob/main/LICENSE)
[](https://github.com/X-D-Lab/KarmaVLM/stargazers)
[](https://github.com/X-D-Lab/KarmaVLM/fork)
[](https://github.com/X-D-Lab/KarmaVLM/graphs/contributors)
</div>
# 👏 Introduction
[KarmaVLM](https://github.com/X-D-Lab/KarmaVLM) is a family of high efficiency and powerful visual language model (VLM) pretrained with interleaved image-text data at scale, enabling content comprehension, recognition, and multi-round conversations about images.
# 🎉 News
* [2024/02] KarmaVLM is released.
# ⚡️Features
KarmaVLM offers the following features:
- **High Efficiency**: KarmaVLM focuses on exploring the capabilities of small parametric quantitative models on multimodal tasks. So, KarmaVLM can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones.
- **Multi-round text-image conversations**: KarmaVLM can take both text and images as inputs and produce text outputs. Currently, it supports multi-round visual question answering with one image.
- **Strong image comprehension**: KarmaVLM is adept at analyzing visuals, making it an efficient tool for tasks like extracting, organizing, and summarizing information from images.
# 👨💻 Quick Start
## Requirements and Installation
```
git clone https://github.com/X-D-Lab/KarmaVLM.git
cd KarmaVLM
conda create -n karmavlm python=3.10 -y
conda activate karmavlm
pip install --upgrade pip # enable PEP 660 support
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
```
## 🌏 Demo
1. CLI Inference
```
python -m llava.serve.cli \
--model-path /path/to/karmavlm/model \
--image-file /path/to/the/test/image
```
2. Gradio Web UI
- Starting the Controller
```
python -m llava.serve.gradio_web_server \
--controller http://localhost:10000 \
--model-list-mode reload
--share ##(optional)
```
- Launching the Gradio Web Server
```
python -m llava.serve.model_worker \
--host 0.0.0.0 \
--controller http://localhost:10000 \
--port 40000 \
--worker http://localhost:40000 \
--model-path /path/to/karmavlm/model \
```
# 📋 License
This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the [Apache license 2.0](./LICENSE).
# 🙇 Architecture
We build our project based on [LLaVA](https://github.com/haotian-liu/LLaVA): Large Language and Vision Assistant.
|
szenthel/Enlighten_Instruct | szenthel | 2024-03-17T10:23:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-03-17T10:23:13Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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[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
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#### Metrics
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### Results
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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- **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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.9.0 |
ferrazzipietro/Mistral-7B-Instruct-v0.2__adapters_en.layer1_4_torch.bfloat16_16_32_0.05_4_0.0002 | ferrazzipietro | 2024-03-17T10:22:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T10:22:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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|
imobarak/Enlighten_Instruct | imobarak | 2024-03-17T10:17:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-03-17T10:17:29Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
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<!-- 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]
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#### Summary
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<!-- 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]
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### Framework versions
- PEFT 0.9.0 |
kaitchup/TheMayonnaise | kaitchup | 2024-03-17T10:09:57Z | 92 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-27T23:02:00Z | ---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
model-index:
- name: TheMayonnaise
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 73.46
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.46
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.88
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 69.19
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 84.29
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaitchup/TheMayonnaise
name: Open LLM Leaderboard
---
# Model Card for Model ID
This is a mixture of experts created with [mergekit](https://github.com/cg123/mergekit) and based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
## Model Details
The model was created using a recipe detailed in this article:
[The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging
](https://kaitchup.substack.com/p/the-mayonnaise-rank-first-on-the)
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** Causal
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Model Sources
Created with mergekit with this configuration:
```
models:
- model: mncai/mistral-7b-dpo-v5
# no parameters necessary for base model
- model: kaitchup/Mayonnaise-4in1-02
parameters:
density: 0.5
weight: 0.3
- model: BarryFutureman/NeuralTurdusVariant1-7B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: mncai/mistral-7b-dpo-v5
parameters:
normalize: true
dtype: float16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kaitchup__TheMayonnaise)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.94|
|AI2 Reasoning Challenge (25-Shot)|73.46|
|HellaSwag (10-Shot) |88.46|
|MMLU (5-Shot) |64.88|
|TruthfulQA (0-shot) |69.19|
|Winogrande (5-shot) |84.29|
|GSM8k (5-shot) |69.37|
|
kaitchup/Mayonnaise-4in1-03 | kaitchup | 2024-03-17T10:09:20Z | 148 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-27T13:19:41Z | ---
license: apache-2.0
language:
- en
tags:
- merge
library_name: transformers
---
# Model Card for Model ID
This is a mixture of experts created with [mergekit](https://github.com/cg123/mergekit) and based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1).
## Model Details
The model was created using a recipe detailed in this article:
[The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging
](https://kaitchup.substack.com/p/the-mayonnaise-rank-first-on-the)
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **Model type:** Causal
- **Language(s) (NLP):** English
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Model Sources
Created with mergekit with this configuration:
```
models:
- model: mncai/mistral-7b-dpo-v5
# no parameters necessary for base model
- model: flemmingmiguel/MBX-7B
parameters:
density: 0.3
weight: 0.3
- model: BarryFutureman/NeuralTurdusVariant1-7B
parameters:
density: 0.3
weight: 0.5
merge_method: ties
base_model: mncai/mistral-7b-dpo-v5
parameters:
normalize: true
dtype: float16
``` |
areegtarek/idefics-9b-all | areegtarek | 2024-03-17T10:07:24Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"idefics",
"image-text-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | image-text-to-text | 2024-03-17T07:35:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
fadilmk/my-pet-cat | fadilmk | 2024-03-17T10:02:28Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-17T09:33:55Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-cat Dreambooth model trained by fadilmk following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 23B426
Sample pictures of this concept:
|
CaptainPollutionTV/CaptainPollution-DSM225 | CaptainPollutionTV | 2024-03-17T09:58:49Z | 0 | 0 | null | [
"DreamBooth",
"Dark Sushi Mix v2.25",
"license:cc",
"region:us"
] | null | 2024-03-10T11:04:18Z | ---
license: cc
tags:
- DreamBooth
- Dark Sushi Mix v2.25
---
Made by CaptainPollutionTV using the getimg.ai Dreambooth tool.
Details about the model:
Base Model
Dark Sushi Mix v2.25
Instance prompt
captainpollution
Class prompt
a man
Learning Rate
0.000001
Learning Rate Scheduler
polynomial
Training Steps
10000 (200 steps warmup)
Class images
1000
Model seed
1411677734
Sample images:
 |
NicolasYn/ppo-SnowballTarget | NicolasYn | 2024-03-17T09:55:11Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2024-03-17T09:55:07Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: NicolasYn/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
quirky-lats-at-mats/LAT-4 | quirky-lats-at-mats | 2024-03-17T09:53:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T09:53:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
quirky-lats-at-mats/LAT-2 | quirky-lats-at-mats | 2024-03-17T09:53:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T09:53:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
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<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
automerger/Experiment28Strangemerges_32-7B | automerger | 2024-03-17T09:49:40Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:Gille/StrangeMerges_32-7B-slerp",
"base_model:merge:Gille/StrangeMerges_32-7B-slerp",
"base_model:yam-peleg/Experiment28-7B",
"base_model:merge:yam-peleg/Experiment28-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T09:48:47Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- yam-peleg/Experiment28-7B
- Gille/StrangeMerges_32-7B-slerp
---
# Experiment28Strangemerges_32-7B
Experiment28Strangemerges_32-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [yam-peleg/Experiment28-7B](https://huggingface.co/yam-peleg/Experiment28-7B)
* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Experiment28-7B
layer_range: [0, 32]
- model: Gille/StrangeMerges_32-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment28-7B
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
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment28Strangemerges_32-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
JiaxiJiang/dreambooth_dog | JiaxiJiang | 2024-03-17T09:48:57Z | 0 | 1 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-17T08:59:28Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: CompVis/stable-diffusion-v1-4
inference: true
instance_prompt: a photo of sks dog
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - JiaxiJiang/dreambooth_dog
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
jitendra89/shawgpt-ft | jitendra89 | 2024-03-17T09:43:58Z | 2 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-03-17T09:43:52Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: shawgpt-ft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# shawgpt-ft
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2317
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.6482 | 0.92 | 3 | 4.2317 |
| 4.6249 | 1.85 | 6 | 4.2317 |
| 4.6434 | 2.77 | 9 | 4.2317 |
| 3.4776 | 4.0 | 13 | 4.2317 |
| 4.6526 | 4.92 | 16 | 4.2317 |
| 4.6328 | 5.85 | 19 | 4.2317 |
| 4.6468 | 6.77 | 22 | 4.2317 |
| 3.4559 | 8.0 | 26 | 4.2317 |
| 4.6135 | 8.92 | 29 | 4.2317 |
| 3.0683 | 9.23 | 30 | 4.2317 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
HinaBl/Coconut | HinaBl | 2024-03-17T09:34:11Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-03-17T09:31:16Z | ---
license: openrail
---
<center><strong>Coconut<br>
[]() |
Dricz/gun-obj-detection-4 | Dricz | 2024-03-17T09:31:41Z | 196 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:gun-object-detection",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-17T08:30:12Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- gun-object-detection
model-index:
- name: gun-obj-detection-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gun-obj-detection-4
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the gun-object-detection dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
danfeg/AceGPT-13B-chat_finetuned_1500 | danfeg | 2024-03-17T09:31:21Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T08:57:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
duxx/gemma_test_lora_model_func_call | duxx | 2024-03-17T09:28:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T09:27:21Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** duxx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
QEQ1996/sad | QEQ1996 | 2024-03-17T09:27:57Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:bigscience-openrail-m",
"region:us"
] | text-to-image | 2024-03-17T09:26:55Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
very funny photo,RAW photo, very long shot, long distance view, ((very tall
extremely muscular gigantic young 18 year old huge-bulged guy)) stand
together at his ordinary cozy villa lounge room at Russia together with tiny
shrunken sexy teacher, cleavage, she wear nice plain simple sundress, very
puffy lips, ((natural beauty)), (shocked female expression:1.2), (detailed
skin:1.15), high platform heels, nice high platform heels,ultra highres,
photorealistic, high detailed RAW photo, detailed face, (realistic skin
texture:1.1), depth of field, very big eyes, DSLR, film grain, natural
beautiful lighting, <lora:add-detail-xl:0.9>, size difference, (size
difference:1.3), the gigantic huge-bulged guy stand with very short tiny
shrunken teacher, he is much bigger than tiny sexy teacher, he has enormous
gigantic biceps <lora:ahxl_v1:0.8> he is twice bigger than tiny sexy
teacher, he is muscular giant compared to the tiny teacher, the sexy teacher
is shocked, vouge fashion, nicely dressed, nice clothes, the shocked
shrunken sexy teacher is so tiny compared to the gigantic huge-bulged guy,
she is much smaller than the gigantic guy, teacher is twice shorter than the
guy, he is so gigantic compared to the teacher, the shocked shrunken teacher
is so small and anorexic, size difference is really huge, his gigantic
biceps are much bigger than the shocked shrunken teacher, the gigantic
muscular guy is really giant, the teacher is anorexic shrunken sexy doll
<lora:shrunk_xl:0.8>, his huge bulge is much bigger than the shocked
shrunken teacher, his huge hung is much bigger than the teacher, the teacher
is so tiny sexy doll, the gigantic guy is hung like a horse, his gigantic
biceps much taller than her entire body <lora:bulge:0.8> the gigantic guy is
much younger than the teacher, he is really hung like horse, enormous giant
biceps, he has enormous giant bulge, the gigantic guy really have giant
enormous bulge, the teacher is so shrunken, the teacher is really tiny
shrunken sexy doll
parameters:
negative_prompt: >-
(((make up, eyeliner))), (deformed iris, deformed pupils, semi-realistic,
cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up,
cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly,
duplicate, morbid, mutilated, (extra fingers:1.2), (mutated hands:1.2),
mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions,
extra limbs, cloned face, disfigured, gross proportions, malformed limbs,
missing arms, missing legs, extra arms, extra legs, (fused fingers:1.2),
(too many fingers:1.2), long neck, (((extra limbs))), ((((small
breasts)))), (((child, kid))), ((((fat)))), (((teacher is muscular))),
((((solo, alone)))), ((((medium shot, portrait)))) unaestheticXL_Alb2,
((((young milf))))
output:
url: images/00001-3846095755.png
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: null
license: bigscience-openrail-m
---
# asd
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/QEQ1996/sad/tree/main) them in the Files & versions tab.
|
HinaBl/Yukitsune-Mifuyu | HinaBl | 2024-03-17T09:25:01Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-03-17T08:38:49Z | ---
license: openrail
---
<center><strong>Yukitsune Mifuyu Model<br>
[](https://www.twitch.tv/mifuyu) |
ugshanyu/new_tokenizer_trained_on_book_dataset | ugshanyu | 2024-03-17T09:18:22Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-09T09:38:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ResplendentAI/Persephone_7B | ResplendentAI | 2024-03-17T09:17:15Z | 114 | 5 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T09:03:36Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
license: other
language:
- en
---
# Persephone
After being in a bit of a rut, I decided to take a radically different approach to produce something new and exciting. It seems to have worked out. I hope you enjoy! |
jchen1234567/Sent_Model_2 | jchen1234567 | 2024-03-17T09:07:21Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-17T08:41:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
dhiva100/a2c-PandaReachDense-v3 | dhiva100 | 2024-03-17T08:57:16Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-17T08:49:33Z | ---
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: -0.24 +/- 0.11
name: mean_reward
verified: false
---
# **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
...
```
|
rk212/my_awesome_model | rk212 | 2024-03-17T08:37:31Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-17T08:26:52Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
PetroGPT/Gemma-10.2B | PetroGPT | 2024-03-17T08:31:27Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-16T16:04:23Z | ---
license: apache-2.0
tags:
- gemma
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
EnmingZhang/PSALM | EnmingZhang | 2024-03-17T08:29:49Z | 89 | 5 | transformers | [
"transformers",
"safetensors",
"llava_phi",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-17T07:47:31Z | ---
license: apache-2.0
---
# PSALM: Pixelwise SegmentAtion with Large Multi-Modal Model
### Features
* A powerful extension of the Large Multi-modal Model for generic (panoptic, instance, semantic) segmentation, referring segmentation and interactivate segmentation.
* Support joint training across multiple segmentation tasks and visual-language tasks.
* Demonstrates zero-shot capabilities on unseen task, such as open-vocabulary segmentation, generalizaed referring segmentation, and video object segmentation.
### Note
You need to change `mm_vision_tower` to your mask2former checkpoint path. |
XuanXuanXuanXuan/Llama-2-13b-hf-gpt-4-80k | XuanXuanXuanXuan | 2024-03-17T08:16:40Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-03-17T08:11:17Z | ---
license: apache-2.0
---
## Description
This model is finetuned on the distillation data from GPT-4.
The base model is meta-llama/Llama-2-13b-hf
## Usage
The model has a query format as in llama-2.
```
<s> [INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{query} [/INST]
``` |
dvdmrs09/gemma-py2 | dvdmrs09 | 2024-03-17T08:15:18Z | 4 | 0 | peft | [
"peft",
"tensorboard",
"keras",
"safetensors",
"gemma",
"generated_from_trainer",
"base_model:google/gemma-7b",
"base_model:adapter:google/gemma-7b",
"license:other",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-03-12T21:51:17Z | ---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: google/gemma-7b
model-index:
- name: gemma-python
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
# use google/gemma-7b if you have access
base_model: google/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
# huggingface repo
datasets:
- path: ./dataset/data1.jsonl
type: input_output
val_set_size: 0.1
output_dir: ./gemma-python
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 10
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# gemma-python
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1143
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 19.0016 | 0.12 | 1 | 18.6992 |
| 19.4686 | 0.25 | 2 | 16.2578 |
| 11.468 | 0.5 | 4 | 8.2891 |
| 7.5305 | 0.75 | 6 | 5.8847 |
| 5.7572 | 1.0 | 8 | 4.3635 |
| 4.3903 | 1.25 | 10 | 3.2849 |
| 2.9497 | 1.5 | 12 | 2.8539 |
| 2.8738 | 1.75 | 14 | 2.6203 |
| 2.7298 | 2.0 | 16 | 2.4534 |
| 2.4284 | 2.25 | 18 | 2.3077 |
| 2.394 | 2.5 | 20 | 2.1876 |
| 2.069 | 2.75 | 22 | 2.1294 |
| 1.9355 | 3.0 | 24 | 2.1048 |
| 1.9635 | 3.25 | 26 | 2.0707 |
| 2.092 | 3.5 | 28 | 2.0596 |
| 1.9675 | 3.75 | 30 | 2.0287 |
| 1.9693 | 4.0 | 32 | 2.0220 |
| 2.0198 | 4.25 | 34 | 2.0124 |
| 1.9357 | 4.5 | 36 | 1.9946 |
| 1.8147 | 4.75 | 38 | 1.9979 |
| 1.9084 | 5.0 | 40 | 1.9751 |
| 1.6678 | 5.25 | 42 | 2.0049 |
| 1.7639 | 5.5 | 44 | 1.9885 |
| 1.7475 | 5.75 | 46 | 1.9777 |
| 1.4848 | 6.0 | 48 | 1.9939 |
| 1.3065 | 6.25 | 50 | 2.0264 |
| 1.4792 | 6.5 | 52 | 2.0125 |
| 1.4233 | 6.75 | 54 | 2.0204 |
| 1.2534 | 7.0 | 56 | 2.0318 |
| 1.2409 | 7.25 | 58 | 2.0445 |
| 1.4309 | 7.5 | 60 | 2.0641 |
| 1.1622 | 7.75 | 62 | 2.0633 |
| 1.228 | 8.0 | 64 | 2.0930 |
| 1.3076 | 8.25 | 66 | 2.1077 |
| 1.2323 | 8.5 | 68 | 2.1060 |
| 1.1635 | 8.75 | 70 | 2.1039 |
| 1.261 | 9.0 | 72 | 2.1068 |
| 1.0122 | 9.25 | 74 | 2.1110 |
| 1.218 | 9.5 | 76 | 2.1180 |
| 1.1022 | 9.75 | 78 | 2.1226 |
| 1.2072 | 10.0 | 80 | 2.1143 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.0 |
Raymondxzr/bert_fill_mask | Raymondxzr | 2024-03-17T08:05:49Z | 126 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-03-17T07:47:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## 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
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### 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
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[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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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Msallam/food_classifier | Msallam | 2024-03-17T08:05:22Z | 63 | 0 | transformers | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-17T07:47:32Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Msallam/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Msallam/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3702
- Validation Loss: 0.3379
- Train Accuracy: 0.92
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7227 | 1.5661 | 0.87 | 0 |
| 1.1928 | 0.7994 | 0.898 | 1 |
| 0.6931 | 0.4948 | 0.92 | 2 |
| 0.4776 | 0.3702 | 0.936 | 3 |
| 0.3702 | 0.3379 | 0.92 | 4 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Tokenizers 0.15.2
|
halee9/translation_ko_en | halee9 | 2024-03-17T07:54:47Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-ko-en",
"base_model:finetune:Helsinki-NLP/opus-mt-ko-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-16T23:24:59Z | ---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: translation_ko_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# translation_ko_en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3551
- Bleu: 26.9822
- Gen Len: 16.97
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 1.3414 | 1.0 | 75000 | 1.3842 | 26.4589 | 16.9755 |
| 1.2335 | 2.0 | 150000 | 1.3551 | 26.9822 | 16.97 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Nitral-Archive/Prima-LelantaclesV7-experimentalv2-7b | Nitral-Archive | 2024-03-17T07:53:12Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b",
"base_model:merge:ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b",
"base_model:tavtav/eros-7b-test",
"base_model:merge:tavtav/eros-7b-test",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-17T07:30:29Z | ---
base_model:
- tavtav/eros-7b-test
- ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b
library_name: transformers
tags:
- mergekit
- merge
license: other
---
### Models Merged
The following models were included in the merge:
* [tavtav/eros-7b-test](https://huggingface.co/tavtav/eros-7b-test)
* [ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b](https://huggingface.co/ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b
layer_range: [0, 32]
- model: tavtav/eros-7b-test
layer_range: [0, 32]
merge_method: slerp
base_model: ChaoticNeutrals/Prima-LelantaclesV7-experimental-7b
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
``` |
n1kolAI/a2c-PandaReachDense-v3 | n1kolAI | 2024-03-17T07:52:45Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-16T21:32:32Z | ---
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: -0.26 +/- 0.10
name: mean_reward
verified: false
---
# **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
...
```
|
AlignmentResearch/robust_llm_pythia-imdb-1.4b-mz-ada-v3 | AlignmentResearch | 2024-03-17T07:44:48Z | 69 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1.4b-deduped",
"base_model:finetune:EleutherAI/pythia-1.4b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-17T07:42:07Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1.4b-deduped
model-index:
- name: robust_llm_pythia-imdb-1.4b-mz-ada-v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-imdb-1.4b-mz-ada-v3
This model is a fine-tuned version of [EleutherAI/pythia-1.4b-deduped](https://huggingface.co/EleutherAI/pythia-1.4b-deduped) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
johnnyluhk/poca-SoccerTwos | johnnyluhk | 2024-03-17T07:43:42Z | 39 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2024-03-17T07:43:22Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: johnnyluhk/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mrbios/q-FrozenLake-v1-4x4-noSlippery | mrbios | 2024-03-17T07:28:43Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-17T05:23:26Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mrbios/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"])
```
|
saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties | saucam | 2024-03-17T07:19:55Z | 49 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"kaist-ai/mistral-orpo-beta",
"mlabonne/NeuralBeagle14-7B",
"conversational",
"base_model:kaist-ai/mistral-orpo-beta",
"base_model:merge:kaist-ai/mistral-orpo-beta",
"base_model:mlabonne/NeuralBeagle14-7B",
"base_model:merge:mlabonne/NeuralBeagle14-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-16T14:54:15Z | ---
tags:
- merge
- mergekit
- lazymergekit
- kaist-ai/mistral-orpo-beta
- mlabonne/NeuralBeagle14-7B
base_model:
- kaist-ai/mistral-orpo-beta
- mlabonne/NeuralBeagle14-7B
license: apache-2.0
---
# mistral-orpo-beta-NeuralBeagle14-7B-dare-ties
mistral-orpo-beta-NeuralBeagle14-7B-dare-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaist-ai/mistral-orpo-beta](https://huggingface.co/kaist-ai/mistral-orpo-beta)
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
## 🧩 Configuration
```yaml
models:
- model: kaist-ai/mistral-orpo-beta
parameters:
density: 0.5
weight: 0.6
# No parameters necessary for base model
- model: mlabonne/NeuralBeagle14-7B
parameters:
density: 0.5
weight: 0.4
merge_method: dare_ties
base_model: kaist-ai/mistral-orpo-beta
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-ties"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
Evaluation results for openllm benchmark via [llm-autoeval](https://github.com/mlabonne/llm-autoeval)
https://gist.github.com/saucam/dcc1f43acce8179f476afc2d91be53ff |
mohsinmubaraksk/Beast-Mixed | mohsinmubaraksk | 2024-03-17T06:59:15Z | 6 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"HuggingFaceH4/zephyr-7b-beta",
"conversational",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:merge:HuggingFaceH4/zephyr-7b-beta",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:merge:mistralai/Mistral-7B-Instruct-v0.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-16T17:58:12Z | ---
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- HuggingFaceH4/zephyr-7b-beta
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- HuggingFaceH4/zephyr-7b-beta
---
# Beast-Mixed
Beast-Mixed is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: HuggingFaceH4/zephyr-7b-beta
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mohsinmubaraksk/Beast-Mixed"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
e22vvb/ALL_mt5-base_15_wikiSQL_sch | e22vvb | 2024-03-17T06:58:58Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-16T11:40:03Z | ---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
model-index:
- name: ALL_mt5-base_15_wikiSQL_sch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ALL_mt5-base_15_wikiSQL_sch
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0585
- Rouge2 Precision: 0.8836
- Rouge2 Recall: 0.8038
- Rouge2 Fmeasure: 0.8358
## 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: 15
- eval_batch_size: 15
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:------:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.0796 | 1.0 | 8637 | 0.0675 | 0.8604 | 0.78 | 0.8122 |
| 0.0683 | 2.0 | 17274 | 0.0617 | 0.8681 | 0.7878 | 0.8199 |
| 0.0587 | 3.0 | 25911 | 0.0593 | 0.8733 | 0.7924 | 0.8248 |
| 0.0527 | 4.0 | 34548 | 0.0579 | 0.8776 | 0.795 | 0.8282 |
| 0.0478 | 5.0 | 43185 | 0.0573 | 0.8788 | 0.7981 | 0.8305 |
| 0.0453 | 6.0 | 51822 | 0.0571 | 0.8806 | 0.7999 | 0.8323 |
| 0.043 | 7.0 | 60459 | 0.0571 | 0.8816 | 0.8008 | 0.8333 |
| 0.0399 | 8.0 | 69096 | 0.0570 | 0.881 | 0.8006 | 0.8329 |
| 0.0389 | 9.0 | 77733 | 0.0573 | 0.8823 | 0.8019 | 0.8343 |
| 0.0363 | 10.0 | 86370 | 0.0573 | 0.8828 | 0.8025 | 0.8347 |
| 0.0366 | 11.0 | 95007 | 0.0580 | 0.8835 | 0.8028 | 0.8352 |
| 0.0333 | 12.0 | 103644 | 0.0579 | 0.8836 | 0.8032 | 0.8355 |
| 0.0325 | 13.0 | 112281 | 0.0581 | 0.8833 | 0.8036 | 0.8356 |
| 0.0327 | 14.0 | 120918 | 0.0585 | 0.8839 | 0.8039 | 0.836 |
| 0.0306 | 15.0 | 129555 | 0.0585 | 0.8836 | 0.8038 | 0.8358 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
mrbios/ppo-Huggy | mrbios | 2024-03-17T06:55:26Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-03-17T06:54:16Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: mrbios/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MYMM/roberta-base | MYMM | 2024-03-17T06:51:07Z | 0 | 0 | peft | [
"peft",
"pytorch",
"arxiv:1910.09700",
"base_model:uer/roberta-base-chinese-extractive-qa",
"base_model:adapter:uer/roberta-base-chinese-extractive-qa",
"region:us"
] | null | 2024-03-17T06:22:50Z | ---
library_name: peft
base_model: uer/roberta-base-chinese-extractive-qa
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.9.0 |
Changgil/K2S3-SOLAR-11b-v3.0 | Changgil | 2024-03-17T06:23:27Z | 221 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-14T00:34:19Z | ---
license: cc-by-nc-4.0
language:
- ko
---
---
## Developed by :
* K2S3
## Model Number:
* K2S3-SOLAR-11b-v3.0
## Base Model :
* [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0)
### Training Data
* The training data for this model includes the Standard Korean Dictionary, training data from KULLM at Korea University, abstracts of master's and doctoral theses, Korean language samples from AI Hub, alpaca-gpt4-data, and samples from The OpenOrca Dataset.
* 이 모델의 훈련 데이터에는 표준국어대사전, 고려대학교 KULLM에서 제공한 훈련 데이터, 석사 및 박사학위 논문의 초록, AI Hub에서 제공한 한국어 데이터 샘플, alpaca-gpt4-data, 그리고 OpenOrca Dataset에서 제공한 샘플들이 포함됩니다.
### Training Method
* This model was fine-tuned on the "upstage/SOLAR-10.7B-v1.0" base model using a full parameter tuning method with SFT (Supervised Fine-Tuning).
* 이 모델은 "upstage/SOLAR-10.7B-v1.0" 기반 모델을 SFT를 사용하여 전체 파라미터 조정 방법으로 미세조정되었습니다.
### Hardware
* Hardware: Utilized two A100 (80G*2EA) GPUs for training.
* Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp.
* 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다. |
neopolita/yi-34b-chat-gguf | neopolita | 2024-03-17T06:13:06Z | 14 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-03-17T03:24:43Z | ---
{}
---
# GGUF quants for [**01-ai/Yi-34B-Chat**](https://huggingface.co/01-ai/Yi-34B-Chat) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/01-ai/Yi-34B-Chat)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
Dricz/gun-obj-detection-2 | Dricz | 2024-03-17T06:06:45Z | 231 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:gun-object-detection",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-17T05:51:08Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- gun-object-detection
model-index:
- name: gun-obj-detection-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gun-obj-detection-2
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the gun-object-detection dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
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