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null | null | {} | andyzeeinpe/ModelsXL | null | [
"region:us"
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null | null | {} | hongbinyang/init_tryout | null | [
"region:us"
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|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9675
- Matthews Correlation: 0.5347
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.3536 | 1.0 | 535 | 0.4962 | 0.4748 |
| 0.2332 | 2.0 | 1070 | 0.6062 | 0.5024 |
| 0.1681 | 3.0 | 1605 | 0.7948 | 0.5262 |
| 0.137 | 4.0 | 2140 | 0.9381 | 0.5023 |
| 0.0804 | 5.0 | 2675 | 0.9675 | 0.5347 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["matthews_correlation"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": []}]} | GIdM/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:32:16+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### 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
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[More Information Needed]
## Training Details
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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]
#### 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. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/CAWD041 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:32:30+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[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]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/CAWD645 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:32:34+00:00 |
text-generation | transformers |
## Model Details
We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format.
## Other Resources
[Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ConvRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Retriever](https://huggingface.co/nvidia/dragon-multiturn-query-encoder)
## Benchmark Results
Results in ConvRAG Bench are as follows:
| | ChatQA-1.0-7B | Command-R-Plus | Llama-3-instruct-70b | GPT-4-0613 | ChatQA-1.0-70B | ChatQA-1.5-8B | ChatQA-1.5-70B |
| -- |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| Doc2Dial | 37.88 | 33.51 | 37.88 | 34.16 | 38.9 | 39.33 | 41.26 |
| QuAC | 29.69 | 34.16 | 36.96 | 40.29 | 41.82 | 39.73 | 38.82 |
| QReCC | 46.97 | 49.77 | 51.34 | 52.01 | 48.05 | 49.03 | 51.40 |
| CoQA | 76.61 | 69.71 | 76.98 | 77.42 | 78.57 | 76.46 | 78.44 |
| DoQA | 41.57 | 40.67 | 41.24 | 43.39 | 51.94 | 49.6 | 50.67 |
| ConvFinQA | 51.61 | 71.21 | 76.6 | 81.28 | 73.69 | 78.46 | 81.88 |
| SQA | 61.87 | 74.07 | 69.61 | 79.21 | 69.14 | 73.28 | 83.82 |
| TopioCQA | 45.45 | 53.77 | 49.72 | 45.09 | 50.98 | 49.96 | 55.63 |
| HybriDial* | 54.51 | 46.7 | 48.59 | 49.81 | 56.44 | 65.76 | 68.27 |
| INSCIT | 30.96 | 35.76 | 36.23 | 36.34 | 31.9 | 30.1 | 32.31 |
| Average (all) | 47.71 | 50.93 | 52.52 | 53.90 | 54.14 | 55.17 | 58.25 |
| Average (exclude HybriDial) | 46.96 | 51.40 | 52.95 | 54.35 | 53.89 | 53.99 | 57.14 |
Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench).
## Prompt Format
<pre>
System: {System}
{Context}
User: {Question}
Assistant: {Response}
User: {Question}
Assistant:
</pre>
## How to use
### take the whole document as context
This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "nvidia/Llama3-ChatQA-1.5-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
messages = [
{"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"}
]
document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n| $ in millions, except earnings per share | Q4 FY24 | Q3 FY24 | Q4 FY23 | Q/Q | Y/Y |\n| Revenue | $22,103 | $18,120 | $6,051 | Up 22% | Up 265% |\n| Gross margin | 76.0% | 74.0% | 63.3% | Up 2.0 pts | Up 12.7 pts |\n| Operating expenses | $3,176 | $2,983 | $2,576 | Up 6% | Up 23% |\n| Operating income | $13,615 | $10,417 | $1,257 | Up 31% | Up 983% |\n| Net income | $12,285 | $9,243 | $1,414 | Up 33% | Up 769% |\n| Diluted earnings per share | $4.93 | $3.71 | $0.57 | Up 33% | Up 765% |"""
def get_formatted_input(messages, context):
system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
instruction = "Please give a full and complete answer for the question."
for item in messages:
if item['role'] == "user":
## only apply this instruction for the first user turn
item['content'] = instruction + " " + item['content']
break
conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
formatted_input = system + "\n\n" + context + "\n\n" + conversation
return formatted_input
formatted_input = get_formatted_input(messages, document)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### run retrieval to get top-n chunks as context
This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B/tree/main/docs) for users to play with.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
import torch
import json
## load ChatQA-1.5 tokenizer and model
model_id = "nvidia/Llama3-ChatQA-1.5-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
## load retriever tokenizer and model
retriever_tokenizer = AutoTokenizer.from_pretrained('nvidia/dragon-multiturn-query-encoder')
query_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-query-encoder')
context_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-context-encoder')
## prepare documents, we take landrover car manual document that we provide as an example
chunk_list = json.load(open("docs.json"))['landrover']
messages = [
{"role": "user", "content": "how to connect the bluetooth in the car?"}
]
### running retrieval
## convert query into a format as follows:
## user: {user}\nagent: {agent}\nuser: {user}
formatted_query_for_retriever = '\n'.join([turn['role'] + ": " + turn['content'] for turn in messages]).strip()
query_input = retriever_tokenizer(formatted_query_for_retriever, return_tensors='pt')
ctx_input = retriever_tokenizer(chunk_list, padding=True, truncation=True, max_length=512, return_tensors='pt')
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
## Compute similarity scores using dot product and rank the similarity
similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx)
ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx)
## get top-n chunks (n=5)
retrieved_chunks = [chunk_list[idx] for idx in ranked_results.tolist()[0][:5]]
context = "\n\n".join(retrieved_chunks)
### running text generation
formatted_input = get_formatted_input(messages, context)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Correspondence to
Zihan Liu ([email protected]), Wei Ping ([email protected])
## Citation
<pre>
@article{liu2024chatqa,
title={ChatQA: Building GPT-4 Level Conversational QA Models},
author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
journal={arXiv preprint arXiv:2401.10225},
year={2024}}
</pre>
## License
The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
| {"language": ["en"], "license": "llama3", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "pipeline_tag": "text-generation"} | nvidia/Llama3-ChatQA-1.5-8B | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"en",
"arxiv:2401.10225",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-04-28T21:32:50+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** zz-xx
- **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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "gguf"], "base_model": "unsloth/gemma-7b-bnb-4bit"} | zz-xx/gemma-7b-bnb-4bit-bias-f16 | null | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:32:54+00:00 |
null | null | {} | AlexsandroAvelar/Teste | null | [
"region:us"
] | null | 2024-04-28T21:35:06+00:00 |
|
audio-classification | transformers | {} | Selma323/first_full_dys_model | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:38:13+00:00 |
|
text-generation | transformers |
## StableGPT4-Micro-1.6B
StableGPT4-Micro-1.6B is a conversational AI model trained on the ChatGPT chats, designed to generate text-based responses for various conversational scenarios. This model has been fine-tuned and optimized for stability and reliability, providing coherent and contextually appropriate responses across a wide range of topics.
### Usage
You can interact with the StableGPT4-Micro-1.6B model by providing it with prompts in JSON format. Each prompt should include the role of the speaker ("user" or "assistant") and the text of their message.
### Example
```json
{"role": "user", "text": "Hi"}
{"role": "assistant", "text": "Hello! How can I help you today? If you have any questions or need assistance, feel free to ask."}
{"role": "user", "text": "write hello world in c++"}
{"role": "assistant", "text": "Here's a simple `HelloWorld` program in C++:\n```cpp\n#include <iostream>\n\nint main() {\n std::cout << \"Hello, World!\" << std::endl;\n return 0;\n}\n```\nThis code uses the `<iostream>` standard library to create a `std::cout` stream and print the string \"Hello, World!\" to the standard output. The `return 0` statement indicates that the program has finished running and returns the control to the user. You can run this program on your C++ compiler's command line or integrated development environment (IDE) to see the output."}
```
### Test
You can use gen.py to test the model.
### Notes
- The model is trained to provide helpful and informative responses based on the input prompts.
- While the model strives to maintain coherence and relevance, it may occasionally generate unexpected or nonsensical outputs.
Please feel free to experiment with the model and provide feedback on its performance and usability. | {"library_name": "transformers", "datasets": ["openchat/openchat_sharegpt4_dataset", "ifmain/gpt4myself"], "base_model": "stabilityai/stablelm-2-zephyr-1_6b", "pipeline_tag": "text-generation"} | ifmain/StableGPT4-Micro-1.6B | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:ifmain/gpt4myself",
"base_model:stabilityai/stablelm-2-zephyr-1_6b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:38:54+00:00 |
null | null | {} | AndyZBY/warmup01 | null | [
"region:us"
] | null | 2024-04-28T21:39:16+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# monacan-translator-test
This model is a fine-tuned version of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "cc-by-nc-4.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "mlabonne/NeuralMonarch-7B", "model-index": [{"name": "monacan-translator-test", "results": []}]} | yleo/monacan-translator-test | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mlabonne/NeuralMonarch-7B",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-04-28T21:41:26+00:00 |
null | null | {"license": "openrail++"} | EthanRhys/Mona-Current | null | [
"license:openrail++",
"region:us"
] | null | 2024-04-28T21:42:19+00:00 |
|
null | null | {} | Starkate/nine | null | [
"region:us"
] | null | 2024-04-28T21:42:41+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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
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[More Information Needed]
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#### 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]
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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]
#### Factors
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[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]
- **Cloud Provider:** [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]
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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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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/vxli9uo | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:43:01+00:00 |
null | null | {} | amches/testmodel | null | [
"region:us"
] | null | 2024-04-28T21:43:10+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | bradleyg223/sai_finetune | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:43:21+00:00 |
text2text-generation | transformers | {"license": "apache-2.0"} | Suru/flant5_gpttweets | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:43:30+00:00 |
|
text-generation | transformers |
## Model Details
We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augmented generation (RAG). Llama3-ChatQA-1.5 is developed using an improved training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Specifically, we incorporate more conversational QA data to enhance its tabular and arithmetic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format.
## Other Resources
[Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ConvRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Retriever](https://huggingface.co/nvidia/dragon-multiturn-query-encoder)
## Benchmark Results
Results in ConvRAG Bench are as follows:
| | ChatQA-1.0-7B | Command-R-Plus | Llama-3-instruct-70b | GPT-4-0613 | ChatQA-1.0-70B | ChatQA-1.5-8B | ChatQA-1.5-70B |
| -- |:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| Doc2Dial | 37.88 | 33.51 | 37.88 | 34.16 | 38.9 | 39.33 | 41.26 |
| QuAC | 29.69 | 34.16 | 36.96 | 40.29 | 41.82 | 39.73 | 38.82 |
| QReCC | 46.97 | 49.77 | 51.34 | 52.01 | 48.05 | 49.03 | 51.40 |
| CoQA | 76.61 | 69.71 | 76.98 | 77.42 | 78.57 | 76.46 | 78.44 |
| DoQA | 41.57 | 40.67 | 41.24 | 43.39 | 51.94 | 49.6 | 50.67 |
| ConvFinQA | 51.61 | 71.21 | 76.6 | 81.28 | 73.69 | 78.46 | 81.88 |
| SQA | 61.87 | 74.07 | 69.61 | 79.21 | 69.14 | 73.28 | 83.82 |
| TopioCQA | 45.45 | 53.77 | 49.72 | 45.09 | 50.98 | 49.96 | 55.63 |
| HybriDial* | 54.51 | 46.7 | 48.59 | 49.81 | 56.44 | 65.76 | 68.27 |
| INSCIT | 30.96 | 35.76 | 36.23 | 36.34 | 31.9 | 30.1 | 32.31 |
| Average (all) | 47.71 | 50.93 | 52.52 | 53.90 | 54.14 | 55.17 | 58.25 |
| Average (exclude HybriDial) | 46.96 | 51.40 | 52.95 | 54.35 | 53.89 | 53.99 | 57.14 |
Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. ChatQA-1.5 used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench).
## Prompt Format
<pre>
System: {System}
{Context}
User: {Question}
Assistant: {Response}
User: {Question}
Assistant:
</pre>
## How to use
### take the whole document as context
This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "nvidia/Llama3-ChatQA-1.5-70B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
messages = [
{"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"}
]
document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n| $ in millions, except earnings per share | Q4 FY24 | Q3 FY24 | Q4 FY23 | Q/Q | Y/Y |\n| Revenue | $22,103 | $18,120 | $6,051 | Up 22% | Up 265% |\n| Gross margin | 76.0% | 74.0% | 63.3% | Up 2.0 pts | Up 12.7 pts |\n| Operating expenses | $3,176 | $2,983 | $2,576 | Up 6% | Up 23% |\n| Operating income | $13,615 | $10,417 | $1,257 | Up 31% | Up 983% |\n| Net income | $12,285 | $9,243 | $1,414 | Up 33% | Up 769% |\n| Diluted earnings per share | $4.93 | $3.71 | $0.57 | Up 33% | Up 765% |"""
def get_formatted_input(messages, context):
system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context."
instruction = "Please give a full and complete answer for the question."
for item in messages:
if item['role'] == "user":
## only apply this instruction for the first user turn
item['content'] = instruction + " " + item['content']
break
conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:"
formatted_input = system + "\n\n" + context + "\n\n" + conversation
return formatted_input
formatted_input = get_formatted_input(messages, document)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### run retrieval to get top-n chunks as context
This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B/tree/main/docs) for users to play with.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
import torch
import json
## load ChatQA-1.5 tokenizer and model
model_id = "nvidia/Llama3-ChatQA-1.5-70B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
## load retriever tokenizer and model
retriever_tokenizer = AutoTokenizer.from_pretrained('nvidia/dragon-multiturn-query-encoder')
query_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-query-encoder')
context_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-context-encoder')
## prepare documents, we take landrover car manual document that we provide as an example
chunk_list = json.load(open("docs.json"))['landrover']
messages = [
{"role": "user", "content": "how to connect the bluetooth in the car?"}
]
### running retrieval
## convert query into a format as follows:
## user: {user}\nagent: {agent}\nuser: {user}
formatted_query_for_retriever = '\n'.join([turn['role'] + ": " + turn['content'] for turn in messages]).strip()
query_input = retriever_tokenizer(formatted_query_for_retriever, return_tensors='pt')
ctx_input = retriever_tokenizer(chunk_list, padding=True, truncation=True, max_length=512, return_tensors='pt')
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]
## Compute similarity scores using dot product and rank the similarity
similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx)
ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx)
## get top-n chunks (n=5)
retrieved_chunks = [chunk_list[idx] for idx in ranked_results.tolist()[0][:5]]
context = "\n\n".join(retrieved_chunks)
### running text generation
formatted_input = get_formatted_input(messages, context)
tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators)
response = outputs[0][tokenized_prompt.input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
## Correspondence to
Zihan Liu ([email protected]), Wei Ping ([email protected])
## Citation
<pre>
@article{liu2024chatqa,
title={ChatQA: Building GPT-4 Level Conversational QA Models},
author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},
journal={arXiv preprint arXiv:2401.10225},
year={2024}}
</pre>
## License
The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)
| {"language": ["en"], "license": "llama3", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "pipeline_tag": "text-generation"} | nvidia/Llama3-ChatQA-1.5-70B | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"en",
"arxiv:2401.10225",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:44:57+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-wikisql
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 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 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "t5-small-finetuned-wikisql", "results": []}]} | MrinmoySaikia/t5-small-finetuned-wikisql | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:45:00+00:00 |
null | null | {} | MinaTransformers/distilbert-base-uncased-finetuned-emotion | null | [
"region:us"
] | null | 2024-04-28T21:46:18+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/ABF003 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:46:44+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/ABF002 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:46:49+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/finalc1 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:48:37+00:00 |
text-to-image | diffusers |
<!-- 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. -->
# Textual inversion text2image fine-tuning - janetsw/par
These are textual inversion adaption weights for stabilityai/stable-diffusion-2-1-base. You can find some example images in the following.
## 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] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "textual_inversion", "diffusers-training"], "base_model": "stabilityai/stable-diffusion-2-1-base", "inference": true} | janetsw/par | null | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-28T21:52:02+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/st2uah3 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:52:07+00:00 |
text-generation | transformers | # Undi95/Llama-3-LewdPlay-8B-evo AWQ
- Model creator: [Undi95](https://huggingface.co/Undi95)
- Original model: [Llama-3-LewdPlay-8B-evo](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B-evo)
## Model Summary
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The new EVOLVE merge method was used (on MMLU specifically), see below for more information!
Unholy was used for uncensoring, Roleplay Llama 3 for the DPO train he got on top, and LewdPlay for the... lewd side.
## How to use
### Prompt template: Llama3
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
```
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Llama-3-LewdPlay-8B-evo-AWQ"
system_message = "You are Llama-3-LewdPlay-8B-evo, incarnated as a powerful AI. You were created by Undi95."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge", "4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "base_model": ["vicgalle/Roleplay-Llama-3-8B", "Undi95/Llama-3-Unholy-8B-e4", "Undi95/Llama-3-LewdPlay-8B"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/Llama-3-LewdPlay-8B-evo-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"base_model:vicgalle/Roleplay-Llama-3-8B",
"base_model:Undi95/Llama-3-Unholy-8B-e4",
"base_model:Undi95/Llama-3-LewdPlay-8B",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:52:39+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/ly3f777 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T21:53:51+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/mc1nxga | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:54:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | liquid9212/dajxf8b | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:54:42+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Aaron82352/length_generalization_testing_ABC_100steps_masking_prompt | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T21:56:53+00:00 |
null | null | {} | ZyanLeyy/gpt2-wikitext2 | null | [
"region:us"
] | null | 2024-04-28T22:00:42+00:00 |
|
null | null |
<!-- 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. -->
# G0428HMA17
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4638 | 0.09 | 10 | 1.3793 |
| 0.7418 | 0.18 | 20 | 0.2028 |
| 0.1674 | 0.27 | 30 | 0.1537 |
| 0.1515 | 0.36 | 40 | 0.1513 |
| 0.1467 | 0.45 | 50 | 0.1468 |
| 0.1476 | 0.54 | 60 | 0.1490 |
| 0.1497 | 0.63 | 70 | 0.1493 |
| 0.1505 | 0.73 | 80 | 0.1493 |
| 0.1436 | 0.82 | 90 | 0.1560 |
| 0.1488 | 0.91 | 100 | 0.1536 |
| 0.1518 | 1.0 | 110 | 0.1515 |
| 0.1436 | 1.09 | 120 | 0.1541 |
| 0.1891 | 1.18 | 130 | 0.1701 |
| 0.1629 | 1.27 | 140 | 0.1505 |
| 0.1532 | 1.36 | 150 | 0.1500 |
| 0.1458 | 1.45 | 160 | 0.1529 |
| 0.1515 | 1.54 | 170 | 0.1505 |
| 0.1493 | 1.63 | 180 | 0.1487 |
| 0.1484 | 1.72 | 190 | 0.1506 |
| 0.1474 | 1.81 | 200 | 0.1488 |
| 0.1484 | 1.9 | 210 | 0.1474 |
| 0.1477 | 1.99 | 220 | 0.1485 |
| 0.1459 | 2.08 | 230 | 0.1478 |
| 0.1467 | 2.18 | 240 | 0.1494 |
| 0.1459 | 2.27 | 250 | 0.1479 |
| 0.1471 | 2.36 | 260 | 0.1482 |
| 0.1446 | 2.45 | 270 | 0.1478 |
| 0.144 | 2.54 | 280 | 0.1479 |
| 0.1445 | 2.63 | 290 | 0.1478 |
| 0.1455 | 2.72 | 300 | 0.1476 |
| 0.1459 | 2.81 | 310 | 0.1475 |
| 0.1454 | 2.9 | 320 | 0.1475 |
| 0.1467 | 2.99 | 330 | 0.1475 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA17", "results": []}]} | Litzy619/G0428HMA17 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:01:09+00:00 |
null | null |
<!-- 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. -->
# G0428HMA18
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4638 | 0.09 | 10 | 1.3793 |
| 0.7418 | 0.18 | 20 | 0.2028 |
| 0.1674 | 0.27 | 30 | 0.1537 |
| 0.1515 | 0.36 | 40 | 0.1513 |
| 0.1467 | 0.45 | 50 | 0.1468 |
| 0.1476 | 0.54 | 60 | 0.1490 |
| 0.1497 | 0.63 | 70 | 0.1493 |
| 0.1505 | 0.73 | 80 | 0.1493 |
| 0.1436 | 0.82 | 90 | 0.1560 |
| 0.1488 | 0.91 | 100 | 0.1536 |
| 0.1518 | 1.0 | 110 | 0.1515 |
| 0.1436 | 1.09 | 120 | 0.1541 |
| 0.1891 | 1.18 | 130 | 0.1701 |
| 0.1629 | 1.27 | 140 | 0.1505 |
| 0.1532 | 1.36 | 150 | 0.1500 |
| 0.1458 | 1.45 | 160 | 0.1529 |
| 0.1515 | 1.54 | 170 | 0.1505 |
| 0.1493 | 1.63 | 180 | 0.1487 |
| 0.1484 | 1.72 | 190 | 0.1506 |
| 0.1474 | 1.81 | 200 | 0.1488 |
| 0.1484 | 1.9 | 210 | 0.1474 |
| 0.1477 | 1.99 | 220 | 0.1485 |
| 0.1459 | 2.08 | 230 | 0.1478 |
| 0.1467 | 2.18 | 240 | 0.1494 |
| 0.1459 | 2.27 | 250 | 0.1479 |
| 0.1471 | 2.36 | 260 | 0.1482 |
| 0.1446 | 2.45 | 270 | 0.1478 |
| 0.144 | 2.54 | 280 | 0.1479 |
| 0.1445 | 2.63 | 290 | 0.1478 |
| 0.1455 | 2.72 | 300 | 0.1476 |
| 0.1459 | 2.81 | 310 | 0.1475 |
| 0.1454 | 2.9 | 320 | 0.1475 |
| 0.1467 | 2.99 | 330 | 0.1475 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA18", "results": []}]} | Litzy619/G0428HMA18 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:01:24+00:00 |
null | null |
<!-- 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. -->
# G0428HMA19
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4638 | 0.09 | 10 | 1.3793 |
| 0.7418 | 0.18 | 20 | 0.2028 |
| 0.1674 | 0.27 | 30 | 0.1537 |
| 0.1515 | 0.36 | 40 | 0.1513 |
| 0.1467 | 0.45 | 50 | 0.1468 |
| 0.1476 | 0.54 | 60 | 0.1490 |
| 0.1497 | 0.63 | 70 | 0.1493 |
| 0.1505 | 0.73 | 80 | 0.1493 |
| 0.1436 | 0.82 | 90 | 0.1560 |
| 0.1488 | 0.91 | 100 | 0.1536 |
| 0.1518 | 1.0 | 110 | 0.1515 |
| 0.1436 | 1.09 | 120 | 0.1541 |
| 0.1891 | 1.18 | 130 | 0.1701 |
| 0.1629 | 1.27 | 140 | 0.1505 |
| 0.1532 | 1.36 | 150 | 0.1500 |
| 0.1458 | 1.45 | 160 | 0.1529 |
| 0.1515 | 1.54 | 170 | 0.1505 |
| 0.1493 | 1.63 | 180 | 0.1487 |
| 0.1484 | 1.72 | 190 | 0.1506 |
| 0.1474 | 1.81 | 200 | 0.1488 |
| 0.1484 | 1.9 | 210 | 0.1474 |
| 0.1477 | 1.99 | 220 | 0.1485 |
| 0.1459 | 2.08 | 230 | 0.1478 |
| 0.1467 | 2.18 | 240 | 0.1494 |
| 0.1459 | 2.27 | 250 | 0.1479 |
| 0.1471 | 2.36 | 260 | 0.1482 |
| 0.1446 | 2.45 | 270 | 0.1478 |
| 0.144 | 2.54 | 280 | 0.1479 |
| 0.1445 | 2.63 | 290 | 0.1478 |
| 0.1455 | 2.72 | 300 | 0.1476 |
| 0.1459 | 2.81 | 310 | 0.1475 |
| 0.1454 | 2.9 | 320 | 0.1475 |
| 0.1467 | 2.99 | 330 | 0.1475 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA19", "results": []}]} | Litzy619/G0428HMA19 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:01:38+00:00 |
null | null |
<!-- 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. -->
# G0428HMA20
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1475
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.4638 | 0.09 | 10 | 1.3793 |
| 0.7418 | 0.18 | 20 | 0.2028 |
| 0.1674 | 0.27 | 30 | 0.1537 |
| 0.1515 | 0.36 | 40 | 0.1513 |
| 0.1467 | 0.45 | 50 | 0.1468 |
| 0.1476 | 0.54 | 60 | 0.1490 |
| 0.1497 | 0.63 | 70 | 0.1493 |
| 0.1505 | 0.73 | 80 | 0.1493 |
| 0.1436 | 0.82 | 90 | 0.1560 |
| 0.1488 | 0.91 | 100 | 0.1536 |
| 0.1518 | 1.0 | 110 | 0.1515 |
| 0.1436 | 1.09 | 120 | 0.1541 |
| 0.1891 | 1.18 | 130 | 0.1701 |
| 0.1629 | 1.27 | 140 | 0.1505 |
| 0.1532 | 1.36 | 150 | 0.1500 |
| 0.1458 | 1.45 | 160 | 0.1529 |
| 0.1515 | 1.54 | 170 | 0.1505 |
| 0.1493 | 1.63 | 180 | 0.1487 |
| 0.1484 | 1.72 | 190 | 0.1506 |
| 0.1474 | 1.81 | 200 | 0.1488 |
| 0.1484 | 1.9 | 210 | 0.1474 |
| 0.1477 | 1.99 | 220 | 0.1485 |
| 0.1459 | 2.08 | 230 | 0.1478 |
| 0.1467 | 2.18 | 240 | 0.1494 |
| 0.1459 | 2.27 | 250 | 0.1479 |
| 0.1471 | 2.36 | 260 | 0.1482 |
| 0.1446 | 2.45 | 270 | 0.1478 |
| 0.144 | 2.54 | 280 | 0.1479 |
| 0.1445 | 2.63 | 290 | 0.1478 |
| 0.1455 | 2.72 | 300 | 0.1476 |
| 0.1459 | 2.81 | 310 | 0.1475 |
| 0.1454 | 2.9 | 320 | 0.1475 |
| 0.1467 | 2.99 | 330 | 0.1475 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA20", "results": []}]} | Litzy619/G0428HMA20 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:02:04+00:00 |
null | null | {"license": "openrail"} | otmanabs/kickoff | null | [
"safetensors",
"license:openrail",
"region:us"
] | null | 2024-04-28T22:02:21+00:00 |
|
reinforcement-learning | sample-factory |
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r HusseinEid/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
| {"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "9.56 +/- 4.15", "name": "mean_reward", "verified": false}]}]}]} | HusseinEid/rl_course_vizdoom_health_gathering_supreme | null | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-28T22:02:46+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/e5x9rb2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:03:11+00:00 |
text-to-image | diffusers |
# SDXL LoRA DreamBooth - kzaleskaa/vector-graphics
<Gallery />
## Model description
These are kzaleskaa/vector-graphics LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use a woman in szn style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kzaleskaa/vector-graphics/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "a cat in szn style", "output": {"url": "image_0.png"}}, {"text": "a cat in szn style", "output": {"url": "image_1.png"}}, {"text": "a cat in szn style", "output": {"url": "image_2.png"}}, {"text": "a cat in szn style", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a woman in szn style"} | kzaleskaa/vector-graphics | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-28T22:03:19+00:00 |
null | null | {} | Litzy619/G0428HMA21 | null | [
"region:us"
] | null | 2024-04-28T22:04:33+00:00 |
|
null | null |
<!-- 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. -->
# G0428HMA22
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1053
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5672 | 0.09 | 10 | 1.5723 |
| 0.95 | 0.18 | 20 | 0.3053 |
| 0.1975 | 0.27 | 30 | 0.1617 |
| 0.1542 | 0.36 | 40 | 0.1482 |
| 0.1465 | 0.45 | 50 | 0.1487 |
| 0.148 | 0.54 | 60 | 0.1489 |
| 0.1482 | 0.63 | 70 | 0.1472 |
| 0.1491 | 0.73 | 80 | 0.1474 |
| 0.1423 | 0.82 | 90 | 0.1480 |
| 0.1448 | 0.91 | 100 | 0.1481 |
| 0.1481 | 1.0 | 110 | 0.1487 |
| 0.1439 | 1.09 | 120 | 0.1480 |
| 0.1453 | 1.18 | 130 | 0.1486 |
| 0.1463 | 1.27 | 140 | 0.1457 |
| 0.1462 | 1.36 | 150 | 0.1437 |
| 0.1372 | 1.45 | 160 | 0.1387 |
| 0.1398 | 1.54 | 170 | 0.1416 |
| 0.133 | 1.63 | 180 | 0.1324 |
| 0.1316 | 1.72 | 190 | 0.1315 |
| 0.1279 | 1.81 | 200 | 0.1283 |
| 0.1273 | 1.9 | 210 | 0.1243 |
| 0.1232 | 1.99 | 220 | 0.1185 |
| 0.1129 | 2.08 | 230 | 0.1186 |
| 0.1112 | 2.18 | 240 | 0.1143 |
| 0.1046 | 2.27 | 250 | 0.1138 |
| 0.1075 | 2.36 | 260 | 0.1122 |
| 0.1088 | 2.45 | 270 | 0.1098 |
| 0.1046 | 2.54 | 280 | 0.1102 |
| 0.0971 | 2.63 | 290 | 0.1084 |
| 0.0994 | 2.72 | 300 | 0.1072 |
| 0.1019 | 2.81 | 310 | 0.1058 |
| 0.1021 | 2.9 | 320 | 0.1054 |
| 0.1068 | 2.99 | 330 | 0.1053 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA22", "results": []}]} | Litzy619/G0428HMA22 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:04:33+00:00 |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_ner_sensitive_spacy` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.6.1,<3.7.0` |
| **Default Pipeline** | `tok2vec`, `ner` |
| **Components** | `tok2vec`, `ner` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (1 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `APP` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 100.00 |
| `ENTS_P` | 100.00 |
| `ENTS_R` | 100.00 |
| `TOK2VEC_LOSS` | 0.00 |
| `NER_LOSS` | 0.00 | | {"language": ["en"], "tags": ["spacy", "token-classification"]} | MoinKhan3012/en_ner_sensitive_spacy | null | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | null | 2024-04-28T22:04:52+00:00 |
null | null | {} | Litzy619/G0428HMA24 | null | [
"region:us"
] | null | 2024-04-28T22:05:36+00:00 |
|
null | null |
<!-- 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. -->
# G0428HMA23
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1053
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5672 | 0.09 | 10 | 1.5723 |
| 0.95 | 0.18 | 20 | 0.3053 |
| 0.1975 | 0.27 | 30 | 0.1617 |
| 0.1542 | 0.36 | 40 | 0.1482 |
| 0.1465 | 0.45 | 50 | 0.1487 |
| 0.148 | 0.54 | 60 | 0.1489 |
| 0.1482 | 0.63 | 70 | 0.1472 |
| 0.1491 | 0.73 | 80 | 0.1474 |
| 0.1423 | 0.82 | 90 | 0.1480 |
| 0.1448 | 0.91 | 100 | 0.1481 |
| 0.1481 | 1.0 | 110 | 0.1487 |
| 0.1439 | 1.09 | 120 | 0.1480 |
| 0.1453 | 1.18 | 130 | 0.1486 |
| 0.1463 | 1.27 | 140 | 0.1457 |
| 0.1462 | 1.36 | 150 | 0.1437 |
| 0.1372 | 1.45 | 160 | 0.1387 |
| 0.1398 | 1.54 | 170 | 0.1416 |
| 0.133 | 1.63 | 180 | 0.1324 |
| 0.1316 | 1.72 | 190 | 0.1315 |
| 0.1279 | 1.81 | 200 | 0.1283 |
| 0.1273 | 1.9 | 210 | 0.1243 |
| 0.1232 | 1.99 | 220 | 0.1185 |
| 0.1129 | 2.08 | 230 | 0.1186 |
| 0.1112 | 2.18 | 240 | 0.1143 |
| 0.1046 | 2.27 | 250 | 0.1138 |
| 0.1075 | 2.36 | 260 | 0.1122 |
| 0.1088 | 2.45 | 270 | 0.1098 |
| 0.1046 | 2.54 | 280 | 0.1102 |
| 0.0971 | 2.63 | 290 | 0.1084 |
| 0.0994 | 2.72 | 300 | 0.1072 |
| 0.1019 | 2.81 | 310 | 0.1058 |
| 0.1021 | 2.9 | 320 | 0.1054 |
| 0.1068 | 2.99 | 330 | 0.1053 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA23", "results": []}]} | Litzy619/G0428HMA23 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:05:36+00:00 |
null | null | {} | Litzy619/G0428HMA25 | null | [
"region:us"
] | null | 2024-04-28T22:05:36+00:00 |
|
null | null |
<!-- 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. -->
# G0428HMA26
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1123
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.48 | 0.09 | 10 | 1.3998 |
| 0.7559 | 0.18 | 20 | 0.2139 |
| 0.1722 | 0.27 | 30 | 0.1588 |
| 0.1516 | 0.36 | 40 | 0.1510 |
| 0.1466 | 0.45 | 50 | 0.1478 |
| 0.1473 | 0.54 | 60 | 0.1488 |
| 0.148 | 0.63 | 70 | 0.1479 |
| 0.1505 | 0.73 | 80 | 0.1491 |
| 0.1423 | 0.82 | 90 | 0.1494 |
| 0.1466 | 0.91 | 100 | 0.1490 |
| 0.1486 | 1.0 | 110 | 0.1487 |
| 0.1603 | 1.09 | 120 | 0.1975 |
| 0.298 | 1.18 | 130 | 0.6268 |
| 0.362 | 1.27 | 140 | 0.1486 |
| 0.1493 | 1.36 | 150 | 0.1485 |
| 0.1402 | 1.45 | 160 | 0.1469 |
| 0.1442 | 1.54 | 170 | 0.1431 |
| 0.1346 | 1.63 | 180 | 0.1396 |
| 0.1405 | 1.72 | 190 | 0.1378 |
| 0.132 | 1.81 | 200 | 0.1358 |
| 0.1309 | 1.9 | 210 | 0.1311 |
| 0.1266 | 1.99 | 220 | 0.1243 |
| 0.119 | 2.08 | 230 | 0.1219 |
| 0.1183 | 2.18 | 240 | 0.1208 |
| 0.114 | 2.27 | 250 | 0.1188 |
| 0.1129 | 2.36 | 260 | 0.1212 |
| 0.1152 | 2.45 | 270 | 0.1179 |
| 0.105 | 2.54 | 280 | 0.1157 |
| 0.106 | 2.63 | 290 | 0.1125 |
| 0.1033 | 2.72 | 300 | 0.1127 |
| 0.1074 | 2.81 | 310 | 0.1123 |
| 0.1098 | 2.9 | 320 | 0.1123 |
| 0.1091 | 2.99 | 330 | 0.1123 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428HMA26", "results": []}]} | Litzy619/G0428HMA26 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T22:06:15+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
galactica-125m - bnb 4bits
- Model creator: https://huggingface.co/facebook/
- Original model: https://huggingface.co/facebook/galactica-125m/
Original model description:
---
license: cc-by-nc-4.0
tags:
- galactica
widget:
- text: "The Transformer architecture [START_REF]"
- text: "The Schwarzschild radius is defined as: \\["
- text: "A force of 0.6N is applied to an object, which accelerates at 3m/s. What is its mass? <work>"
- text: "Lecture 1: The Ising Model\n\n"
- text: "[START_I_SMILES]"
- text: "[START_AMINO]GHMQSITAGQKVISKHKNGRFYQCEVVRLTTETFYEVNFDDGSFSDNLYPEDIVSQDCLQFGPPAEGEVVQVRWTDGQVYGAKFVASHPIQMYQVEFEDGSQLVVKRDDVYTLDEELP[END_AMINO] ## Keywords"
inference: false
---
# GALACTICA 125M (mini)
Model card from the original [repo](https://github.com/paperswithcode/galai/blob/main/docs/model_card.md)
Following [Mitchell et al. (2018)](https://arxiv.org/abs/1810.03993), this model card provides information about the GALACTICA model, how it was trained, and the intended use cases. Full details about how the model was trained and evaluated can be found in the [release paper](https://galactica.org/paper.pdf).
## Model Details
The GALACTICA models are trained on a large-scale scientific corpus. The models are designed to perform scientific tasks, including but not limited to citation prediction, scientific QA, mathematical reasoning, summarization, document generation, molecular property prediction and entity extraction. The models were developed by the Papers with Code team at Meta AI to study the use of language models for the automatic organization of science. We train models with sizes ranging from 125M to 120B parameters. Below is a summary of the released models:
| Size | Parameters |
|:-----------:|:-----------:|
| `mini` | 125 M |
| `base` | 1.3 B |
| `standard` | 6.7 B |
| `large` | 30 B |
| `huge` | 120 B |
## Release Date
November 2022
## Model Type
Transformer based architecture in a decoder-only setup with a few modifications (see paper for more details).
## Paper & Demo
[Paper](https://galactica.org/paper.pdf) / [Demo](https://galactica.org)
## Model Use
The primary intended users of the GALACTICA models are researchers studying language models applied to the scientific domain. We also anticipate the model will be useful for developers who wish to build scientific tooling. However, we caution against production use without safeguards given the potential of language models to hallucinate.
The models are made available under a non-commercial CC BY-NC 4.0 license. More information about how to use the model can be found in the README.md of this repository.
## Training Data
The GALACTICA models are trained on 106 billion tokens of open-access scientific text and data. This includes papers, textbooks, scientific websites, encyclopedias, reference material, knowledge bases, and more. We tokenize different modalities to provide a natural langauge interface for different tasks. See the README.md for more information. See the paper for full information on the training data.
## How to use
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m")
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto")
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto", torch_dtype=torch.float16)
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto", load_in_8bit=True)
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
## Performance and Limitations
The model outperforms several existing language models on a range of knowledge probes, reasoning, and knowledge-intensive scientific tasks. This also extends to general NLP tasks, where GALACTICA outperforms other open source general language models. That being said, we note a number of limitations in this section.
As with other language models, GALACTICA is often prone to hallucination - and training on a high-quality academic corpus does not prevent this, especially for less popular and less cited scientific concepts. There are no guarantees of truthful output when generating from the model. This extends to specific modalities such as citation prediction. While GALACTICA's citation behaviour approaches the ground truth citation behaviour with scale, the model continues to exhibit a popularity bias at larger scales.
In addition, we evaluated the model on several types of benchmarks related to stereotypes and toxicity. Overall, the model exhibits substantially lower toxicity rates compared to other large language models. That being said, the model continues to exhibit bias on certain measures (see the paper for details). So we recommend care when using the model for generations.
## Broader Implications
GALACTICA can potentially be used as a new way to discover academic literature. We also expect a lot of downstream use for application to particular domains, such as mathematics, biology, and chemistry. In the paper, we demonstrated several examples of the model acting as alternative to standard search tools. We expect a new generation of scientific tools to be built upon large language models such as GALACTICA.
We encourage researchers to investigate beneficial and new use cases for these models. That being said, it is important to be aware of the current limitations of large language models. Researchers should pay attention to common issues such as hallucination and biases that could emerge from using these models.
## Citation
```bibtex
@inproceedings{GALACTICA,
title={GALACTICA: A Large Language Model for Science},
author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic},
year={2022}
}
```
| {} | RichardErkhov/facebook_-_galactica-125m-4bits | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1810.03993",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-28T22:06:57+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
galactica-125m - bnb 8bits
- Model creator: https://huggingface.co/facebook/
- Original model: https://huggingface.co/facebook/galactica-125m/
Original model description:
---
license: cc-by-nc-4.0
tags:
- galactica
widget:
- text: "The Transformer architecture [START_REF]"
- text: "The Schwarzschild radius is defined as: \\["
- text: "A force of 0.6N is applied to an object, which accelerates at 3m/s. What is its mass? <work>"
- text: "Lecture 1: The Ising Model\n\n"
- text: "[START_I_SMILES]"
- text: "[START_AMINO]GHMQSITAGQKVISKHKNGRFYQCEVVRLTTETFYEVNFDDGSFSDNLYPEDIVSQDCLQFGPPAEGEVVQVRWTDGQVYGAKFVASHPIQMYQVEFEDGSQLVVKRDDVYTLDEELP[END_AMINO] ## Keywords"
inference: false
---
# GALACTICA 125M (mini)
Model card from the original [repo](https://github.com/paperswithcode/galai/blob/main/docs/model_card.md)
Following [Mitchell et al. (2018)](https://arxiv.org/abs/1810.03993), this model card provides information about the GALACTICA model, how it was trained, and the intended use cases. Full details about how the model was trained and evaluated can be found in the [release paper](https://galactica.org/paper.pdf).
## Model Details
The GALACTICA models are trained on a large-scale scientific corpus. The models are designed to perform scientific tasks, including but not limited to citation prediction, scientific QA, mathematical reasoning, summarization, document generation, molecular property prediction and entity extraction. The models were developed by the Papers with Code team at Meta AI to study the use of language models for the automatic organization of science. We train models with sizes ranging from 125M to 120B parameters. Below is a summary of the released models:
| Size | Parameters |
|:-----------:|:-----------:|
| `mini` | 125 M |
| `base` | 1.3 B |
| `standard` | 6.7 B |
| `large` | 30 B |
| `huge` | 120 B |
## Release Date
November 2022
## Model Type
Transformer based architecture in a decoder-only setup with a few modifications (see paper for more details).
## Paper & Demo
[Paper](https://galactica.org/paper.pdf) / [Demo](https://galactica.org)
## Model Use
The primary intended users of the GALACTICA models are researchers studying language models applied to the scientific domain. We also anticipate the model will be useful for developers who wish to build scientific tooling. However, we caution against production use without safeguards given the potential of language models to hallucinate.
The models are made available under a non-commercial CC BY-NC 4.0 license. More information about how to use the model can be found in the README.md of this repository.
## Training Data
The GALACTICA models are trained on 106 billion tokens of open-access scientific text and data. This includes papers, textbooks, scientific websites, encyclopedias, reference material, knowledge bases, and more. We tokenize different modalities to provide a natural langauge interface for different tasks. See the README.md for more information. See the paper for full information on the training data.
## How to use
Find below some example scripts on how to use the model in `transformers`:
## Using the Pytorch model
### Running the model on a CPU
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m")
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto")
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
### Running the model on a GPU using different precisions
#### FP16
<details>
<summary> Click to expand </summary>
```python
# pip install accelerate
import torch
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto", torch_dtype=torch.float16)
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
#### INT8
<details>
<summary> Click to expand </summary>
```python
# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, OPTForCausalLM
tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-125m")
model = OPTForCausalLM.from_pretrained("facebook/galactica-125m", device_map="auto", load_in_8bit=True)
input_text = "The Transformer architecture [START_REF]"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
</details>
## Performance and Limitations
The model outperforms several existing language models on a range of knowledge probes, reasoning, and knowledge-intensive scientific tasks. This also extends to general NLP tasks, where GALACTICA outperforms other open source general language models. That being said, we note a number of limitations in this section.
As with other language models, GALACTICA is often prone to hallucination - and training on a high-quality academic corpus does not prevent this, especially for less popular and less cited scientific concepts. There are no guarantees of truthful output when generating from the model. This extends to specific modalities such as citation prediction. While GALACTICA's citation behaviour approaches the ground truth citation behaviour with scale, the model continues to exhibit a popularity bias at larger scales.
In addition, we evaluated the model on several types of benchmarks related to stereotypes and toxicity. Overall, the model exhibits substantially lower toxicity rates compared to other large language models. That being said, the model continues to exhibit bias on certain measures (see the paper for details). So we recommend care when using the model for generations.
## Broader Implications
GALACTICA can potentially be used as a new way to discover academic literature. We also expect a lot of downstream use for application to particular domains, such as mathematics, biology, and chemistry. In the paper, we demonstrated several examples of the model acting as alternative to standard search tools. We expect a new generation of scientific tools to be built upon large language models such as GALACTICA.
We encourage researchers to investigate beneficial and new use cases for these models. That being said, it is important to be aware of the current limitations of large language models. Researchers should pay attention to common issues such as hallucination and biases that could emerge from using these models.
## Citation
```bibtex
@inproceedings{GALACTICA,
title={GALACTICA: A Large Language Model for Science},
author={Ross Taylor and Marcin Kardas and Guillem Cucurull and Thomas Scialom and Anthony Hartshorn and Elvis Saravia and Andrew Poulton and Viktor Kerkez and Robert Stojnic},
year={2022}
}
```
| {} | RichardErkhov/facebook_-_galactica-125m-8bits | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1810.03993",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-28T22:07:18+00:00 |
null | null | {"license": "apache-2.0"} | JaineDev/newmodel-1 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T22:09:14+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/qvy8fl3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:10:23+00:00 |
null | null | {} | eaysu/my_awesome_model | null | [
"region:us"
] | null | 2024-04-28T22:11:06+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
xglm-4.5B - bnb 4bits
- Model creator: https://huggingface.co/facebook/
- Original model: https://huggingface.co/facebook/xglm-4.5B/
Original model description:
---
language:
- multilingual
- en
- ru
- zh
- de
- es
- fr
- ja
- it
- pt
- el
- ko
- fi
- id
- tr
- ar
- vi
- th
- bg
- ca
- hi
- et
- bn
- ta
- ur
- sw
- te
- eu
- my
- ht
- qu
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-4.5B
XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in [this repository](https://github.com/pytorch/fairseq/tree/main/examples/xglm).
## Model card
For intended usage of the model, please refer to the [model card](https://github.com/pytorch/fairseq/blob/main/examples/xglm/model_card.md) released by the XGLM-4.5B development team.
## Example (COPA)
The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.
```python
import torch
import torch.nn.functional as F
from transformers import XGLMTokenizer, XGLMForCausalLM
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-4.5B")
model = XGLMForCausalLM.from_pretrained("facebook/xglm-4.5B")
data_samples = {
'en': [
{
"premise": "I wanted to conserve energy.",
"choice1": "I swept the floor in the unoccupied room.",
"choice2": "I shut off the light in the unoccupied room.",
"question": "effect",
"label": "1"
},
{
"premise": "The flame on the candle went out.",
"choice1": "I blew on the wick.",
"choice2": "I put a match to the wick.",
"question": "cause",
"label": "0"
}
],
'zh': [
{
"premise": "ζζ³θηΊ¦θ½ζΊγ",
"choice1": "ζε¨η©ΊηηζΏι΄ιζ«δΊε°ζΏγ",
"choice2": "ζζη©ΊζΏι΄ιηη―ε
³δΊγ",
"question": "effect",
"label": "1"
},
{
"premise": "θ‘ηδΈηη«η°ηηδΊγ",
"choice1": "ζεΉηδΊη―θ―γ",
"choice2": "ζζδΈζ Ήη«ζ΄ζΎε¨η―θ―δΈγ",
"question": "cause",
"label": "0"
}
],
'hi': [
{
"premise": "M te vle konsève enèji.",
"choice1": "Mwen te fin baleye chanm lib la.",
"choice2": "Mwen te femen limyè nan chanm lib la.",
"question": "effect",
"label": "1"
},
{
"premise": "Flam bouji a te etenn.",
"choice1": "Mwen te soufle bouji a.",
"choice2": "Mwen te limen mèch bouji a.",
"question": "cause",
"label": "0"
}
]
}
def get_logprobs(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:]
outputs = model(**inputs, labels=input_ids)
logits = outputs.logits
logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2))
return logprobs
# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task.
# A return value of 0 indicates that the first alternative is more plausible,
# while 1 indicates that the second alternative is more plausible.
def COPA_eval(prompt, alternative1, alternative2):
lprob1 = get_logprobs(prompt + "\n" + alternative1).sum()
lprob2 = get_logprobs(prompt + "\n" + alternative2).sum()
return 0 if lprob1 > lprob2 else 1
for lang in data_samples_long:
for idx, example in enumerate(data_samples_long[lang]):
predict = COPA_eval(example["premise"], example["choice1"], example["choice2"])
print(f'{lang}-{idx}', predict, example['label'])
# en-0 1 1
# en-1 0 0
# zh-0 1 1
# zh-1 0 0
# hi-0 1 1
# hi-1 0 0
```
| {} | RichardErkhov/facebook_-_xglm-4.5B-4bits | null | [
"transformers",
"safetensors",
"xglm",
"text-generation",
"arxiv:2112.10668",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-04-28T22:11:08+00:00 |
null | null | {"license": "apache-2.0"} | jzebjohnson/Morra | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T22:12:09+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# full_finetuned-code-tinyllama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "model-index": [{"name": "full_finetuned-code-tinyllama", "results": []}]} | anudaw/full_finetuned-code-tinyllama | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:13:25+00:00 |
null | null | {} | PaolaGhione/quantized-qwen1.5-1.88B-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-04-28T22:14:38+00:00 |
|
text-generation | transformers | # flammenai/flammen22C-mistral-7B AWQ
- Model creator: [flammenai](https://huggingface.co/flammenai)
- Original model: [flammen22C-mistral-7B](https://huggingface.co/flammenai/flammen22C-mistral-7B)
## Model Summary
A Mistral 7B LLM built from merging pretrained models and finetuning on [flammenai/casual-conversation-DPO](https://huggingface.co/datasets/flammenai/casual-conversation-DPO).
Flammen specializes in exceptional character roleplay, creative writing, and general intelligence
### Method
Finetuned using an A100 on Google Colab.
[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/flammen22C-mistral-7B-AWQ"
system_message = "You are flammen22C-mistral-7B, incarnated as a powerful AI. You were created by flammenai."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "datasets": ["flammenai/casual-conversation-DPO"], "base_model": ["flammenai/flammen22-mistral-7B"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/flammen22C-mistral-7B-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"dataset:flammenai/casual-conversation-DPO",
"base_model:flammenai/flammen22-mistral-7B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:15:15+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3651
- eval_label_0_f1: 0.7305
- eval_label_0_accuracy: 0.9498
- eval_label_1_f1: 0.8458
- eval_label_1_accuracy: 0.8573
- eval_label_2_f1: 0.7816
- eval_label_2_accuracy: 0.9576
- eval_label_3_f1: 0.7638
- eval_label_3_accuracy: 0.9097
- eval_label_4_f1: 0.6438
- eval_label_4_accuracy: 0.9420
- eval_label_5_f1: 0.6857
- eval_label_5_accuracy: 0.9755
- eval_runtime: 5.7337
- eval_samples_per_second: 156.444
- eval_steps_per_second: 5.058
- epoch: 5.0
- step: 795
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "cardiffnlp/twitter-xlm-roberta-base", "model-index": [{"name": "results", "results": []}]} | parhamabedazad/results | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-xlm-roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:16:08+00:00 |
text-generation | transformers | # flammenai/flammen22-mistral-7B AWQ
- Model creator: [flammenai](https://huggingface.co/flammenai)
- Original model: [flammen22-mistral-7B](https://huggingface.co/flammenai/flammen22-mistral-7B)
## Model Summary
A Mistral 7B LLM built from merging pretrained models and finetuning on [Doctor-Shotgun/theory-of-mind-dpo](https://huggingface.co/datasets/Doctor-Shotgun/theory-of-mind-dpo).
Flammen specializes in exceptional character roleplay, creative writing, and general intelligence
Finetuned using an A100 on Google Colab.
[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/flammen22-mistral-7B-AWQ"
system_message = "You are flammen22-mistral-7B, incarnated as a powerful AI. You were created by flammenai."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "datasets": ["Doctor-Shotgun/theory-of-mind-dpo"], "base_model": ["flammenai/flammen21X-mistral-7B"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/flammen22-mistral-7B-AWQ | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"dataset:Doctor-Shotgun/theory-of-mind-dpo",
"base_model:flammenai/flammen21X-mistral-7B",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:18:05+00:00 |
null | null | {} | Mit1208/kosmos-2-image-caption-trl | null | [
"region:us"
] | null | 2024-04-28T22:18:21+00:00 |
|
null | null | {} | cacaaaaaaaa/harem | null | [
"region:us"
] | null | 2024-04-28T22:19:02+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/sfckfq4 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:20:18+00:00 |
null | transformers |
[<img src="https://ai.hooking.co.il/upload/images/logo/0qUf-dashboard-hookingai-logo.png"/>](https://software.hooking.ltd/)
# Model Card for Monah-8b-gguf
**This is en Experimental model**
## Model Description
- **Developed by:** hooking AI
- **License:** Apache-2.0
- **Original Model:** [Monah-8b](https://huggingface.co/hooking-dev/Monah-8b)
- **Purpose:** The Monah-8b model is designed to generate high-quality, contextually relevant text for various applications.
- utilizing the flexibility of the LLaMA architecture for domain spesific and uncensored utilization.
## Languages
The text in the model is primarily in English, but also other languages.
## Model Structure
### Data Instances
A typical data instance consists of a special proparitary dataset used for training uncensored text generation models.
## Model Creation
### Curation Rationale
The model was curated to create a comprehensive resource for training general-purpose text generation models.
With the sole focus on delivering highly uncensored, accurate and relevant content.
### Source Data
- **Initial Data Collection and Normalization:** Data was generated aprtialy by private models synthetically along with private dataset owned by HookingAI, carefully normalized to maintain consistency and quality.
- **Who are the source language producers?** The text data comes from a variety of llms we trained, including domain experts and general content models available to HookingAI.
-
## Considerations for Using the Data
**This model is not for kids!!**
**The content is uncensored!!**
### Social Impact of Model
This model supports the development of AI models capable of generating contextually accurate, uncensored and nuanced text, contributing to better information dissemination and automation in content creation for specific use.
### Discussion of Biases
As with any model, there's potential for biases and hallucinations. **Also the content may be sexual or illeagal.** Which users should consider when deploying models trained on this data.
### Other Known Limitations
The effectiveness and applicability of the model may be limited by its content diversity and scope.
## Additional Information
**Model Quantization Table**
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| --- | --- | --- | --- | --- | --- |
| SexyLlama-8b-gguf.Q2_K.gguf | Q2_K | 2 | 3.18 GB | 5.66 GB | significant quality loss - not recommended for most purpose |
| SexyLlama-8b-gguf.Q3_K_M.gguf | Q3_K_M | 3 | 4.02 GB | 6.41 GB GB | very small, high quality loss |
| SexyLlama-8b-gguf.Q4_K_S.gguf | Q4_K_S | 4 | 4.69 GB | 6.97 GB | small, greater quality loss |
| SexyLlama-8b-gguf.Q4_K_M.gguf | Q4_K_M | 4 | 4.92 GB | 7.50 GB | medium, balanced quality - recommended |
| SexyLlama-8b-gguf.Q5_0.gguf | Q5_0 | 5 | 5.6 GB | 7.63 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| SexyLlama-8b-gguf.Q5_K_S.gguf | Q5_K_S | 5 | 5.6 GB | 7.93 GB | large, low quality loss - recommended |
| SexyLlama-8b-gguf.Q5_K_M.gguf | Q5_K_M | 5 | 5.73 GB | 8.20 GB | large, very low quality loss - recommended |
| SexyLlama-8b-gguf.Q6_K.gguf | Q6_K | 6 | 6.60 GB | 8.97 GB | very large, extremely low quality loss |
| SexyLlama-8b-gguf.F16 | F16 | 16 | 16.10 GB | 21.48 GB | very large, extremely low quality loss - not recommended |
**WE ARE NOT RESPONSIBLE FOR MISUSE!!**
**Prompt template (ChatML):**
```
<|im_start|>system
This is a conversation between a user and an uncensored AI assistant trained by HookingAI.<|im_end|>
<|im_start|> user
{user prompt}<|im_end|>
<|im_start|>assistant
{assistant response}
```
### System Prompt:
```
This is a conversation between a user and an uncensored AI assistant trained by HookingAI.
```
### Examples:
User:
```
hey, what can you do?
```
Assistant:
```
I can do anything you want me to, but if you're looking for something specific, let me know. What are you in the mood for? Do you want me to be naughty or nice? Or maybe a little bit of both? Let me know and I'll make sure to deliver.
```
User:
```
tell me about your self and your capabilities
```
Assistant:
```
What would you like me to tell you about myself? And my capabilities? Let's start with that.
If you want to know more about me, feel free to ask me anything. But if you're looking for a specific answer, here's a few things that come to mind:
- I am a trained AI assistant trained by HookingAI, so I can provide uncensored responses to your questions.
- I am always up for a good conversation, so feel free to ask me anything you want to know.
- My capabilities include being able to flirt, tease, and seduce you in ways that will make you feel good.
- I also ca help you with daily tasks, just let me know what can I do for you?
```
### Model Curators
The model was curated by Hooking, utilizing their resources to ensure uncensorship and quality.
### Licensing Information
The model is available under the Apache-2.0 license.
## Citation Information
```bib
@inproceedings{hooking2024Monah-8b,
title={Monah-8b: A Domain Specific Model for General-Purpose Text Generation},
author={Hooking AI Team},
year={2024},
publisher={Hooking}
}
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "llama", "gguf", "trl", "sft"], "base_model": "meta-llama/Meta-Llama-3-8B"} | hooking-dev/Monah-8b-gguf | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"trl",
"sft",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:20:39+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
xglm-4.5B - bnb 8bits
- Model creator: https://huggingface.co/facebook/
- Original model: https://huggingface.co/facebook/xglm-4.5B/
Original model description:
---
language:
- multilingual
- en
- ru
- zh
- de
- es
- fr
- ja
- it
- pt
- el
- ko
- fi
- id
- tr
- ar
- vi
- th
- bg
- ca
- hi
- et
- bn
- ta
- ur
- sw
- te
- eu
- my
- ht
- qu
license: mit
thumbnail: https://huggingface.co/front/thumbnails/facebook.png
inference: false
---
# XGLM-4.5B
XGLM-4.5B is a multilingual autoregressive language model (with 4.5 billion parameters) trained on a balanced corpus of a diverse set of 134 languages. It was introduced in the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin\*, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li\* (\*Equal Contribution). The original implementation was released in [this repository](https://github.com/pytorch/fairseq/tree/main/examples/xglm).
## Model card
For intended usage of the model, please refer to the [model card](https://github.com/pytorch/fairseq/blob/main/examples/xglm/model_card.md) released by the XGLM-4.5B development team.
## Example (COPA)
The following snippet shows how to evaluate our models (GPT-3 style, zero-shot) on the Choice of Plausible Alternatives (COPA) task, using examples in English, Chinese and Hindi.
```python
import torch
import torch.nn.functional as F
from transformers import XGLMTokenizer, XGLMForCausalLM
tokenizer = XGLMTokenizer.from_pretrained("facebook/xglm-4.5B")
model = XGLMForCausalLM.from_pretrained("facebook/xglm-4.5B")
data_samples = {
'en': [
{
"premise": "I wanted to conserve energy.",
"choice1": "I swept the floor in the unoccupied room.",
"choice2": "I shut off the light in the unoccupied room.",
"question": "effect",
"label": "1"
},
{
"premise": "The flame on the candle went out.",
"choice1": "I blew on the wick.",
"choice2": "I put a match to the wick.",
"question": "cause",
"label": "0"
}
],
'zh': [
{
"premise": "ζζ³θηΊ¦θ½ζΊγ",
"choice1": "ζε¨η©ΊηηζΏι΄ιζ«δΊε°ζΏγ",
"choice2": "ζζη©ΊζΏι΄ιηη―ε
³δΊγ",
"question": "effect",
"label": "1"
},
{
"premise": "θ‘ηδΈηη«η°ηηδΊγ",
"choice1": "ζεΉηδΊη―θ―γ",
"choice2": "ζζδΈζ Ήη«ζ΄ζΎε¨η―θ―δΈγ",
"question": "cause",
"label": "0"
}
],
'hi': [
{
"premise": "M te vle konsève enèji.",
"choice1": "Mwen te fin baleye chanm lib la.",
"choice2": "Mwen te femen limyè nan chanm lib la.",
"question": "effect",
"label": "1"
},
{
"premise": "Flam bouji a te etenn.",
"choice1": "Mwen te soufle bouji a.",
"choice2": "Mwen te limen mèch bouji a.",
"question": "cause",
"label": "0"
}
]
}
def get_logprobs(prompt):
inputs = tokenizer(prompt, return_tensors="pt")
input_ids, output_ids = inputs["input_ids"], inputs["input_ids"][:, 1:]
outputs = model(**inputs, labels=input_ids)
logits = outputs.logits
logprobs = torch.gather(F.log_softmax(logits, dim=2), 2, output_ids.unsqueeze(2))
return logprobs
# Zero-shot evaluation for the Choice of Plausible Alternatives (COPA) task.
# A return value of 0 indicates that the first alternative is more plausible,
# while 1 indicates that the second alternative is more plausible.
def COPA_eval(prompt, alternative1, alternative2):
lprob1 = get_logprobs(prompt + "\n" + alternative1).sum()
lprob2 = get_logprobs(prompt + "\n" + alternative2).sum()
return 0 if lprob1 > lprob2 else 1
for lang in data_samples_long:
for idx, example in enumerate(data_samples_long[lang]):
predict = COPA_eval(example["premise"], example["choice1"], example["choice2"])
print(f'{lang}-{idx}', predict, example['label'])
# en-0 1 1
# en-1 0 0
# zh-0 1 1
# zh-1 0 0
# hi-0 1 1
# hi-1 0 0
```
| {} | RichardErkhov/facebook_-_xglm-4.5B-8bits | null | [
"transformers",
"safetensors",
"xglm",
"text-generation",
"arxiv:2112.10668",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] | null | 2024-04-28T22:21:11+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
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## Bias, Risks, and Limitations
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[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
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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[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]
- **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. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/m1n1bul | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:21:35+00:00 |
text-generation | transformers | # cognitivecomputations/dolphin-2.9-llama3-8b-256k AWQ
- Model creator: [cognitivecomputations](https://huggingface.co/cognitivecomputations)
- Original model: [dolphin-2.9-llama3-8b-256k](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-256k)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/dolphin-2.9-llama3-8b-256k-AWQ"
system_message = "You are dolphin-2.9-llama3-8b-256k, incarnated as a powerful AI. You were created by cognitivecomputations."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"license": "llama3", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/dolphin-2.9-llama3-8b-256k-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"license:llama3",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:25:07+00:00 |
text-generation | transformers | Quantizations of https://huggingface.co/senseable/WestLake-7B-v2
# From original readme
### Key Features
1. **Role-Play**: Westlake-7Bv2 can seamlessly adapt to different character personas and engage in dynamic conversations while maintaining consistency throughout the interaction. It can generate believable dialogues across various genres, including fiction, non-fiction, historical events, or even fantasy worlds.
2. **Text Generation**: This model is proficient at generating original content such as stories, poems, essays, news articles, and more. Its ability to capture the essence of different writing styles makes it an ideal tool for creative writers seeking inspiration or assistance in their projects.
3. **Contextual Understanding**: Westlake-7B's extensive training allows it to comprehend complex contexts and generate responses that align with given situations. It can handle multiple topics simultaneously, making it versatile across various applications.
4. **Continuous Learning**: As a language model, Westlake-7B continuously improves its performance through ongoing training on new data sets. This ensures its capabilities remain up-to-date and relevant in an ever-evolving world of communication.
## Usage Guidelines
To utilize Westlake-7Bv2 for your projects or experiments, follow these steps:
1. **Prompting**: Provide clear and concise prompts that outline the desired role-play scenario or text generation task. The quality of output depends heavily on the clarity and relevance of input instructions.
2. **Feedback Loop**: For optimal results, consider incorporating a feedback loop into your application to refine generated outputs based on user preferences or additional contextual information. This iterative process can significantly enhance the model's performance in specific domains.
3. **Ethical Considerations**: As with any AI system, ensure responsible usage of Westlake-7B by avoiding harmful content generation or misuse of its capabilities. | {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "WestLake-7B-v2"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/WestLake-7B-v2-imatrix-GGUF | null | [
"transformers",
"gguf",
"imatrix",
"WestLake-7B-v2",
"text-generation",
"en",
"license:other",
"region:us"
] | null | 2024-04-28T22:25:15+00:00 |
null | null | {} | IA-GAB/ITZY | null | [
"region:us"
] | null | 2024-04-28T22:26:23+00:00 |
|
null | null | {} | pouyamohseni/mert-dastgah-lr1 | null | [
"region:us"
] | null | 2024-04-28T22:27:39+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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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]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/xun2tom | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:28:35+00:00 |
text-generation | transformers | {} | Jennny/merged_sft_llama2_pku | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:30:08+00:00 |
|
null | null | {} | 5w4n/test | null | [
"region:us"
] | null | 2024-04-28T22:30:34+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-2000
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2884
- Accuracy: 0.575
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6771 | 0.9912 | 28 | 0.7267 | 0.5225 |
| 0.519 | 1.9823 | 56 | 0.9883 | 0.5275 |
| 0.3645 | 2.9735 | 84 | 1.1386 | 0.5575 |
| 0.3797 | 4.0 | 113 | 2.1208 | 0.525 |
| 0.3047 | 4.9912 | 141 | 1.2884 | 0.575 |
| 0.3335 | 5.9823 | 169 | 2.0830 | 0.5325 |
| 0.3022 | 6.9735 | 197 | 1.8196 | 0.5375 |
| 0.2444 | 8.0 | 226 | 1.6744 | 0.5475 |
| 0.2283 | 8.9912 | 254 | 2.3540 | 0.535 |
| 0.2282 | 9.9115 | 280 | 2.3509 | 0.535 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-2000", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.575, "name": "Accuracy"}]}]}]} | DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-2000 | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:31:14+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/3d7jxwv | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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] | null | 2024-04-28T22:31:58+00:00 |
null | null |
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[More Information Needed] | {} | F3nixh4ck/testchat | null | [
"arxiv:1910.09700",
"region:us"
] | null | 2024-04-28T22:32:12+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/TEK041 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:32:14+00:00 |
null | null | {} | asmaa-ali/Qwen1.5-0.5B-dpo-mix-7k | null | [
"region:us"
] | null | 2024-04-28T22:34:27+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOOwO/final23 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:36:00+00:00 |
object-detection | pytorch |
YoLov8 models for detecting Faces and genitals of Anime images for censoring
Dataset: https://universe.roboflow.com/nyanners-workshop/anime-nsfw-detection
Which contain some models that you can test on the website | {"language": ["en"], "license": "cc-by-2.0", "library_name": "pytorch", "tags": ["Anime", "YOLOv8", "Coral EdgeTPU"], "pipeline_tag": "object-detection"} | creeper8965/Anime-NSFW-Detection | null | [
"pytorch",
"tflite",
"Anime",
"YOLOv8",
"Coral EdgeTPU",
"object-detection",
"en",
"license:cc-by-2.0",
"region:us"
] | null | 2024-04-28T22:36:18+00:00 |
text-to-image | diffusers |
# SDXL LoRA DreamBooth - kzaleskaa/monster_toy
<Gallery />
## Model description
These are kzaleskaa/monster_toy LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use a sbu toy to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kzaleskaa/monster_toy/tree/main) them in the Files & versions tab.
| {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "a sbu toy riding a bicycle", "output": {"url": "image_0.png"}}, {"text": "a sbu toy riding a bicycle", "output": {"url": "image_1.png"}}, {"text": "a sbu toy riding a bicycle", "output": {"url": "image_2.png"}}, {"text": "a sbu toy riding a bicycle", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a sbu toy"} | kzaleskaa/monster_toy | null | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-28T22:36:37+00:00 |
text-generation | transformers | {} | wendys-llc/llama3-8b-receipts-v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:37:10+00:00 |
|
text-generation | transformers | # erfanzar/Xerxes-8B-Instruct-v0.4 AWQ
- Model creator: [erfanzar](https://huggingface.co/erfanzar)
- Original model: [Xerxes-8B-Instruct-v0.4](https://huggingface.co/erfanzar/Xerxes-8B-Instruct-v0.4)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Xerxes-8B-Instruct-v0.4-AWQ"
system_message = "You are Xerxes-8B-Instruct-v0.4, incarnated as a powerful AI. You were created by erfanzar."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/Xerxes-8B-Instruct-v0.4-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:37:54+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/JUL792 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:38:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | GamblerOnTrain/JUL786 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:38:41+00:00 |
text-generation | transformers | # jondurbin/bagel-8b-v1.0 AWQ
- Model creator: [jondurbin](https://huggingface.co/jondurbin)
- Original model: [bagel-8b-v1.0](https://huggingface.co/jondurbin/bagel-8b-v1.0)
## Model Summary
The name of this model is "llama-3-bagel-8b-v1.0" and it was built with llama-3 from Meta.
This is a fine-tune of llama-3-8b using the bagel dataset, but instead of 4 prompt formats it's standardized on a single format - llama-3 instruct.
See [bagel](https://github.com/jondurbin/bagel) for additional details on the datasets.
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/bagel-8b-v1.0-AWQ"
system_message = "You are bagel-8b-v1.0, incarnated as a powerful AI. You were created by jondurbin."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"license": "other", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible - llama-3 - bagel"], "datasets": ["ai2_arc", "allenai/ultrafeedback_binarized_cleaned", "argilla/distilabel-intel-orca-dpo-pairs", "jondurbin/airoboros-3.2", "codeparrot/apps", "facebook/belebele", "bluemoon-fandom-1-1-rp-cleaned", "boolq", "camel-ai/biology", "camel-ai/chemistry", "camel-ai/math", "camel-ai/physics", "jondurbin/contextual-dpo-v0.1", "jondurbin/gutenberg-dpo-v0.1", "jondurbin/py-dpo-v0.1", "jondurbin/truthy-dpo-v0.1", "LDJnr/Capybara", "jondurbin/cinematika-v0.1", "WizardLM/WizardLM_evol_instruct_70k", "glaiveai/glaive-function-calling-v2", "jondurbin/gutenberg-dpo-v0.1", "grimulkan/LimaRP-augmented", "lmsys/lmsys-chat-1m", "ParisNeo/lollms_aware_dataset", "TIGER-Lab/MathInstruct", "Muennighoff/natural-instructions", "openbookqa", "kingbri/PIPPA-shareGPT", "piqa", "Vezora/Tested-22k-Python-Alpaca", "ropes", "cakiki/rosetta-code", "Open-Orca/SlimOrca", "b-mc2/sql-create-context", "squad_v2", "mattpscott/airoboros-summarization", "migtissera/Synthia-v1.3", "unalignment/toxic-dpo-v0.2", "WhiteRabbitNeo/WRN-Chapter-1", "WhiteRabbitNeo/WRN-Chapter-2", "winogrande"], "license_name": "llama3", "license_link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE", "base_model": "meta-llama/Meta-Llama-3-8B", "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/bagel-8b-v1.0-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible - llama-3 - bagel",
"conversational",
"dataset:ai2_arc",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"dataset:jondurbin/airoboros-3.2",
"dataset:codeparrot/apps",
"dataset:facebook/belebele",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:camel-ai/biology",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/math",
"dataset:camel-ai/physics",
"dataset:jondurbin/contextual-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:lmsys/lmsys-chat-1m",
"dataset:ParisNeo/lollms_aware_dataset",
"dataset:TIGER-Lab/MathInstruct",
"dataset:Muennighoff/natural-instructions",
"dataset:openbookqa",
"dataset:kingbri/PIPPA-shareGPT",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:ropes",
"dataset:cakiki/rosetta-code",
"dataset:Open-Orca/SlimOrca",
"dataset:b-mc2/sql-create-context",
"dataset:squad_v2",
"dataset:mattpscott/airoboros-summarization",
"dataset:migtissera/Synthia-v1.3",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:WhiteRabbitNeo/WRN-Chapter-1",
"dataset:WhiteRabbitNeo/WRN-Chapter-2",
"dataset:winogrande",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:41:30+00:00 |
null | null | {"license": "openrail"} | Freaky98/mr_puzzles_rvc_v2_300e | null | [
"license:openrail",
"region:us"
] | null | 2024-04-28T22:42:27+00:00 |
|
null | null | {} | Blocktoast64/Chuck-E-Cheese-Voices | null | [
"region:us"
] | null | 2024-04-28T22:43:07+00:00 |
|
text-classification | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | HC-85/distilbert-arxiv-multilabel-b4 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:45:03+00:00 |
null | null | {"license": "mit"} | master-frog/simple-mnist | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2024-04-28T22:46:57+00:00 |
|
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | bryan-tchakote/fine-tuned-model | null | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/y23fl5n | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:48:10+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** e21cseu0315
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-2b-it-bnb-4bit"} | e21cseu0315/finetuned_gemini_2B | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:49:59+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/w64hwyi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:50:02+00:00 |
text-classification | transformers | ## Jupyter Notebooks
GitHub link : [lihuicham/airbnb-helpfulness-classifier](https://github.com/lihuicham/airbnb-helpfulness-classifier)
Fine-tuning Python code in `finetuning.ipynb`
## Team Members (S001 - Synthetic Expert Team E) :
Li Hui Cham, Isaac Sparrow, Christopher Arraya, Nicholas Wong, Lei Zhang, Leonard Yang
## Description
This model is an AirBnB reviews helpfulness classifier. It can predict the helpfulness, from most helpful (A) to least helpful (C) of the reviews on AirBnB website.
## Pre-trained LLM
Our project fine-tuned [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) for multi-class text (sequence) classification.
## Dataset
5000 samples are scraped from AirBnB website based on `listing_id` from this [Kaggle AirBnB Listings & Reviews dataset](https://www.kaggle.com/datasets/mysarahmadbhat/airbnb-listings-reviews).Samples were translated from French to English language.
Training Set : 4560 samples synthetically labelled by GPT-4 Turbo. Cost was approximately $60.
Test/Evaluation Set : 500 samples labelled manually by two groups (each group labelled 250 samples), majority votes applies. A scoring rubrics (shown below) is used for labelling.
## Training Details
```
hyperparameters = {'learning_rate': 3e-05,
'per_device_train_batch_size': 16,
'weight_decay': 1e-04,
'num_train_epochs': 4,
'warmup_steps': 500}
```
We trained our model on Colab Pro which costed us approximately 56 computing units.
## Slides
 | {"tags": ["reviews", "multi-class", "classifier", "text classification", "roberta-base"], "widget": [{"text": "This was my first time getting an Airbnb and won\u2019t be the last! The location was so peaceful and quiet, perfect for a weekend getaway. The space was modern and clean. I was able to cook a whole breakfast buffet in the kitchen. The hosts were extremely helpful and friendly, 10/10 highly recommend! Definitely will be returning when the weather gets warmer!!"}, {"text": "We went for a weekend to be out in nature with our kids and a friend. The house is very cute inside and decorated nicely BUT the property photos leave out a house right next-door, so not private, a messy yard area w broken down sheds and construction, a gun range close by so all we could hear was gunshots all day, the kitchen cabinets esp the pantry were dirty and filled w junk and the hot tub was foggy, dirty and they must have just dumped a lot of bleach in rather than balancing the chemicals and cleaning it properly because everyone got rashes/eye irritation/headaches and had to get out and shower. The house really only sleeps five and you are stuck scrounging for pillows blankets and sheets and blowing up an aero bed for anyone else. The first one had a leak so we had to find a second and do it all again. We could not find a trundle bed. I really wanted to like it as cute as the pictures are but the real thing leaves a lot to be desired."}, {"text": "Was quiet and nice"}]} | lihuicham/airbnb-reviews-helpfulness-classifier-roberta-base | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"reviews",
"multi-class",
"classifier",
"text classification",
"roberta-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:51:44+00:00 |
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-checkpoint-finetuned-pav1
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8461
- Rouge1: 8.8662
- Rouge2: 2.3482
- Rougel: 7.1437
- Rougelsum: 8.1986
- Gen Len: 19.0
## 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:
- learning_rate: 3e-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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 3.2493 | 1.0 | 1250 | 2.9730 | 8.6473 | 2.2038 | 6.9787 | 7.9995 | 19.0 |
| 3.1397 | 2.0 | 2500 | 2.9083 | 8.8119 | 2.332 | 7.0958 | 8.1469 | 19.0 |
| 3.0943 | 3.0 | 3750 | 2.8793 | 8.9522 | 2.4215 | 7.21 | 8.2723 | 19.0 |
| 3.0728 | 4.0 | 5000 | 2.8571 | 8.8992 | 2.3688 | 7.174 | 8.2298 | 19.0 |
| 3.0566 | 5.0 | 6250 | 2.8493 | 8.841 | 2.3368 | 7.1312 | 8.1745 | 19.0 |
| 3.0409 | 6.0 | 7500 | 2.8461 | 8.8662 | 2.3482 | 7.1437 | 8.1986 | 19.0 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "t5-small-checkpoint-finetuned-pav1", "results": []}]} | Racha009/t5-small-checkpoint-finetuned-pav1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:51:50+00:00 |
null | null | {} | Ukado/Syx | null | [
"region:us"
] | null | 2024-04-28T22:53:49+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | likhithasapu/codemix-indicbart-sft | null | [
"transformers",
"safetensors",
"mbart",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:53:56+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/mooncell_v30 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:54:36+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/cmss6lz | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T22:57:22+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-mrpc-glue
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the glue and the mrpc datasets.
It achieves the following results on the evaluation set:
- Loss: 0.7777
- Accuracy: 0.8358
- F1: 0.8810
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.3807 | 1.0893 | 500 | 0.7777 | 0.8358 | 0.8810 |
| 0.245 | 2.1786 | 1000 | 0.8901 | 0.8382 | 0.8846 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["text-classification", "generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "distilroberta-base", "widget": [{"text": "Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion.,Yucaipa bought Dominick's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998.", "example_title": "Not Equivalent"}, {"text": "Revenue in the first quarter of the year dropped 15 percent from the same period a year earlier.,With the scandal hanging over Stewart's company revenue the first quarter of the year dropped 15 percent from the same period a year earlier.", "example_title": "Equivalent"}], "model-index": [{"name": "distilroberta-base-mrpc-glue", "results": []}]} | leovale14/distilroberta-base-mrpc-glue | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T22:58:02+00:00 |
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