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Setpember/Jon_GPT2L_DPO_3props_epi_point5 | Setpember | "2025-03-25T22:07:29" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-25T22:05:54" | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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] |
ModelCloud/GRIN-MoE-gptq-4bit | ModelCloud | "2024-09-19T10:17:43" | 5 | 6 | null | [
"safetensors",
"grinmoe",
"gptq",
"4bit",
"int4",
"gptqmodel",
"modelcloud",
"custom_code",
"4-bit",
"region:us"
] | null | "2024-09-19T09:51:48" | ---
tags:
- gptq
- 4bit
- int4
- gptqmodel
- modelcloud
---
This model has been quantized using [GPTQModel](https://github.com/ModelCloud/GPTQModel).
- **bits**: 4
- **group_size**: 128
- **desc_act**: false
- **static_groups**: false
- **sym**: true
- **lm_head**: false
- **damp_percent**: 0.0025
- **damp_auto_increment**: 0.0015
- **true_sequential**: true
- **model_name_or_path**: ""
- **model_file_base_name**: "model"
- **quant_method**: "gptq"
- **checkpoint_format**: "gptq"
- **meta**：
- **quantizer**: "gptqmodel:1.0.3-dev0"
## Example:
```python
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
model_name = "ModelCloud/GRIN-MoE-gptq-4bit"
prompt = [
{"role": "system",
"content": "You are GRIN-MoE model from microsoft, a helpful assistant."},
{"role": "user", "content": "I am in Shanghai, preparing to visit the natural history museum. Can you tell me the best way to"}
]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = GPTQModel.from_quantized(model_name, trust_remote_code=True)
input_tensor = tokenizer.apply_chat_template(prompt, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
## Lm_eval result:
| Tasks | Metric | | GRIN-MoE | GRIN-MoE-gptq-4bit |
| ------------------------------------- | ---------- | --- | -------- | ------------------ |
| arc_challenge | acc | ↑ | 0.6408 | 0.6425 |
| | acc_norm | ↑ | 0.6561 | 0.6587 |
| arc_easy | acc | ↑ | 0.8645 | 0.8683 |
| | acc_norm | ↑ | 0.8422 | 0.846 |
| boolq | acc | ↑ | 0.8820 | 0.8765 |
| hellaswag | acc | ↑ | 0.6972 | 0.6891 |
| | acc_norm | ↑ | 0.8518 | 0.8486 |
| lambada_openai | acc | ↑ | 0.7058 | 0.7068 |
| | perplexity | ↓ | 3.4568 | 3.5732 |
| mmlu | acc | ↑ | 0.7751 | 0.7706 |
| - humanities | acc | ↑ | 0.7394 | 0.7384 |
| - formal_logic | acc | ↑ | 0.6429 | 0.6746 |
| - high_school_european_history | acc | ↑ | 0.8606 | 0.8364 |
| - high_school_us_history | acc | ↑ | 0.9118 | 0.902 |
| - high_school_world_history | acc | ↑ | 0.8903 | 0.8734 |
| - international_law | acc | ↑ | 0.9256 | 0.9091 |
| - jurisprudence | acc | ↑ | 0.8426 | 0.8519 |
| - logical_fallacies | acc | ↑ | 0.8344 | 0.8528 |
| - moral_disputes | acc | ↑ | 0.7977 | 0.8208 |
| - moral_scenarios | acc | ↑ | 0.6961 | 0.6849 |
| - philosophy | acc | ↑ | 0.8199 | 0.8071 |
| - prehistory | acc | ↑ | 0.8457 | 0.8426 |
| - professional_law | acc | ↑ | 0.6173 | 0.6193 |
| - world_religions | acc | ↑ | 0.8480 | 0.8655 |
| - other | acc | ↑ | 0.8130 | 0.805 |
| - business_ethics | acc | ↑ | 0.8100 | 0.78 |
| - clinical_knowledge | acc | ↑ | 0.8415 | 0.8302 |
| - college_medicine | acc | ↑ | 0.7514 | 0.7457 |
| - global_facts | acc | ↑ | 0.5700 | 0.54 |
| - human_aging | acc | ↑ | 0.7803 | 0.7668 |
| - management | acc | ↑ | 0.8447 | 0.8447 |
| - marketing | acc | ↑ | 0.9145 | 0.9103 |
| - medical_genetics | acc | ↑ | 0.9200 | 0.89 |
| - miscellaneous | acc | ↑ | 0.8966 | 0.8927 |
| - nutrition | acc | ↑ | 0.8333 | 0.8268 |
| - professional_accounting | acc | ↑ | 0.6489 | 0.656 |
| - professional_medicine | acc | ↑ | 0.8750 | 0.8603 |
| - virology | acc | ↑ | 0.5422 | 0.5361 |
| - social sciences | acc | ↑ | 0.8638 | 0.8544 |
| - econometrics | acc | ↑ | 0.5789 | 0.5789 |
| - high_school_geography | acc | ↑ | 0.9091 | 0.8788 |
| - high_school_government_and_politics | acc | ↑ | 0.9585 | 0.943 |
| - high_school_macroeconomics | acc | ↑ | 0.8308 | 0.8103 |
| - high_school_microeconomics | acc | ↑ | 0.9328 | 0.9286 |
| - high_school_psychology | acc | ↑ | 0.9321 | 0.9303 |
| - human_sexuality | acc | ↑ | 0.8779 | 0.8626 |
| - professional_psychology | acc | ↑ | 0.8382 | 0.8219 |
| - public_relations | acc | ↑ | 0.7545 | 0.7727 |
| - security_studies | acc | ↑ | 0.7878 | 0.7918 |
| - sociology | acc | ↑ | 0.8905 | 0.8955 |
| - us_foreign_policy | acc | ↑ | 0.9000 | 0.88 |
| - stem | acc | ↑ | 0.7044 | 0.7031 |
| - abstract_algebra | acc | ↑ | 0.5000 | 0.45 |
| - anatomy | acc | ↑ | 0.7407 | 0.7481 |
| - astronomy | acc | ↑ | 0.8618 | 0.8618 |
| - college_biology | acc | ↑ | 0.8889 | 0.875 |
| - college_chemistry | acc | ↑ | 0.6100 | 0.59 |
| - college_computer_science | acc | ↑ | 0.7100 | 0.67 |
| - college_mathematics | acc | ↑ | 0.5100 | 0.58 |
| - college_physics | acc | ↑ | 0.4608 | 0.4608 |
| - computer_security | acc | ↑ | 0.8200 | 0.82 |
| - conceptual_physics | acc | ↑ | 0.7787 | 0.766 |
| - electrical_engineering | acc | ↑ | 0.6828 | 0.6828 |
| - elementary_mathematics | acc | ↑ | 0.7566 | 0.7593 |
| - high_school_biology | acc | ↑ | 0.9000 | 0.9097 |
| - high_school_chemistry | acc | ↑ | 0.6650 | 0.665 |
| - high_school_computer_science | acc | ↑ | 0.8700 | 0.86 |
| - high_school_mathematics | acc | ↑ | 0.4370 | 0.4296 |
| - high_school_physics | acc | ↑ | 0.5960 | 0.5894 |
| - high_school_statistics | acc | ↑ | 0.7176 | 0.7222 |
| - machine_learning | acc | ↑ | 0.6071 | 0.6339 |
| openbookqa | acc | ↑ | 0.3920 | 0.386 |
| | acc_norm | ↑ | 0.4900 | 0.486 |
| piqa | acc | ↑ | 0.8183 | 0.8166 |
| | acc_norm | ↑ | 0.8205 | 0.8177 |
| rte | acc | ↑ | 0.8014 | 0.7834 |
| truthfulqa_mc1 | acc | ↑ | 0.3880 | 0.399 |
| winogrande | acc | ↑ | 0.7940 | 0.768 |
| | | | | |
| Groups | Metric | | Value | Value |
| mmlu | acc | ↑ | 0.7751 | 0.7706 |
| - humanities | acc | ↑ | 0.7394 | 0.7384 |
| - other | acc | ↑ | 0.8130 | 0.805 |
| - social sciences | acc | ↑ | 0.8638 | 0.8544 |
| - stem | acc | ↑ | 0.7044 | 0.7031 |
|
NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF | NikolayKozloff | "2025-02-24T13:46:13" | 0 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"pl",
"base_model:CYFRAGOVPL/PLLuM-12B-nc-instruct",
"base_model:quantized:CYFRAGOVPL/PLLuM-12B-nc-instruct",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-24T13:45:29" | ---
license: cc-by-nc-4.0
language:
- pl
tags:
- llama-cpp
- gguf-my-repo
base_model: CYFRAGOVPL/PLLuM-12B-nc-instruct
---
# NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF
This model was converted to GGUF format from [`CYFRAGOVPL/PLLuM-12B-nc-instruct`](https://huggingface.co/CYFRAGOVPL/PLLuM-12B-nc-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/CYFRAGOVPL/PLLuM-12B-nc-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF --hf-file pllum-12b-nc-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF --hf-file pllum-12b-nc-instruct-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF --hf-file pllum-12b-nc-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/PLLuM-12B-nc-instruct-Q6_K-GGUF --hf-file pllum-12b-nc-instruct-q6_k.gguf -c 2048
```
|
uproai/Rose-2x7B-GGUF | uproai | "2024-02-26T11:55:30" | 2 | 0 | null | [
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"uproai/ros-7b-v1",
"WizardLM/WizardMath-7B-V1.1",
"base_model:WizardLMTeam/WizardMath-7B-V1.1",
"base_model:merge:WizardLMTeam/WizardMath-7B-V1.1",
"base_model:uproai/ros-7b-v1",
"base_model:merge:uproai/ros-7b-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-02-26T11:50:52" | ---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- uproai/ros-7b-v1
- WizardLM/WizardMath-7B-V1.1
base_model:
- uproai/ros-7b-v1
- WizardLM/WizardMath-7B-V1.1
---
# Rose-2x7B-GGUF
Rose-2x7B-GGUF is GGUF version of [Rose-2x7B](https://huggingface.co/uproai/Rose-2x7B) which is a Mixure of Experts (MoE) made with the following models using [Mergekit](https://github.com/cg123/mergekit):
* [maywell/PiVoT-0.1-Starling-LM-RP](https://huggingface.co/maywell/PiVoT-0.1-Starling-LM-RP)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
## 🧩 Configuration
```yaml
base_model: uproai/ros-7b-v1
experts:
- source_model: maywell/PiVoT-0.1-Starling-LM-RP
positive_prompts:
- "storywriting"
- "write"
- "scene"
- "story"
- "character"
- source_model: WizardLM/WizardMath-7B-V1.1
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
tokenizer_source: union
```
|
AppleHarem/keli | AppleHarem | "2023-12-28T09:28:46" | 0 | 0 | null | [
"art",
"text-to-image",
"license:mit",
"region:us"
] | text-to-image | "2023-12-28T08:21:50" | ---
license: mit
pipeline_tag: text-to-image
tags:
- art
---
# Lora of keli
This model is trained with [kohyass-scripts](https://github.com/kohya-ss/sd-scripts). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs). And the WebUI Panel provide by [LittleAppleWebUI](https://github.com/LittleApple-fp16/LittleAppleWebUI)
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [LittleApple-fp16/SpiritForeseerMix](https://huggingface.co/LittleApple-fp16/SpiritForeseerMix).
The trigger words are:
1. `keli`
2. `bangs, klee_\(genshin_impact\), twintails, ahoge, pointy_ears, low_twintails, long_hair, hair_between_eyes, hat, smile, sidelocks, red_headwear, open_mouth, clover_print, hat_feather, cabbie_hat, light_brown_hair, bag, :d, backpack, hat_ornament, red_eyes, orange_eyes, blonde_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
6. Individuals who feel that writing a WebUI is meaningless or impatient.
These are available epochs:
| Epochs | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | pattern_9 | pattern_10 | bikini | bondage | cheongsam | free | hanfu | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:-----------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:-----------------------------------------------------|:---------------------------------------------|:----------------------------------------------------|:---------------------------------------------------|:-----------------------------------------|:-------------------------------------------|:-----------------------------------------|:-----------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-----------------------------------------|:---------------------------------------------|
| **10** | **0.965** | [**Download**](000010/keli-000010.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000010/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000010/previews/nude.png) | [<NSFW, click to see>](000010/previews/nude2.png) |  |  |
| 9 | 0.965 | [Download](000009/keli-000009.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000009/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000009/previews/nude.png) | [<NSFW, click to see>](000009/previews/nude2.png) |  |  |
| 8 | 0.965 | [Download](000008/keli-000008.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000008/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000008/previews/nude.png) | [<NSFW, click to see>](000008/previews/nude2.png) |  |  |
| 7 | 0.965 | [Download](000007/keli-000007.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000007/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000007/previews/nude.png) | [<NSFW, click to see>](000007/previews/nude2.png) |  |  |
| 6 | 0.964 | [Download](000006/keli-000006.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000006/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000006/previews/nude.png) | [<NSFW, click to see>](000006/previews/nude2.png) |  |  |
| 5 | 0.964 | [Download](000005/keli-000005.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000005/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000005/previews/nude.png) | [<NSFW, click to see>](000005/previews/nude2.png) |  |  |
| 4 | 0.963 | [Download](000004/keli-000004.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000004/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000004/previews/nude.png) | [<NSFW, click to see>](000004/previews/nude2.png) |  |  |
| 3 | 0.963 | [Download](000003/keli-000003.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000003/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000003/previews/nude.png) | [<NSFW, click to see>](000003/previews/nude2.png) |  |  |
| 2 | 0.962 | [Download](000002/keli-000002.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000002/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000002/previews/nude.png) | [<NSFW, click to see>](000002/previews/nude2.png) |  |  |
| 1 | 0.962 | [Download](000001/keli-000001.safetensors) |  |  |  |  |  |  |  |  |  |  |  | [<NSFW, click to see>](000001/previews/bondage.png) |  |  |  |  |  | [<NSFW, click to see>](000001/previews/nude.png) | [<NSFW, click to see>](000001/previews/nude2.png) |  |  |
|
luaqi/sn29_01041 | luaqi | "2025-01-04T02:36:52" | 238 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-04T02:30:32" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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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sb3/ppo-MiniGrid-Empty-Random-5x5-v0 | sb3 | "2023-03-31T18:11:08" | 262 | 0 | stable-baselines3 | [
"stable-baselines3",
"MiniGrid-Empty-Random-5x5-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-28T12:23:13" | ---
library_name: stable-baselines3
tags:
- MiniGrid-Empty-Random-5x5-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: MiniGrid-Empty-Random-5x5-v0
type: MiniGrid-Empty-Random-5x5-v0
metrics:
- type: mean_reward
value: 0.97 +/- 0.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **MiniGrid-Empty-Random-5x5-v0**
This is a trained model of a **PPO** agent playing **MiniGrid-Empty-Random-5x5-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -orga sb3 -f logs/
python -m rl_zoo3.enjoy --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env MiniGrid-Empty-Random-5x5-v0 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('clip_range', 0.2),
('ent_coef', 0.0),
('env_wrapper', 'gym_minigrid.wrappers.FlatObsWrapper'),
('gae_lambda', 0.95),
('gamma', 0.99),
('learning_rate', 0.00025),
('n_envs', 8),
('n_epochs', 10),
('n_steps', 128),
('n_timesteps', 100000.0),
('normalize', True),
('policy', 'MlpPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|
mradermacher/astrollama-3-8b-chat_aic-i1-GGUF | mradermacher | "2024-09-30T12:49:06" | 27 | 0 | transformers | [
"transformers",
"gguf",
"llama-3",
"astronomy",
"astrophysics",
"arxiv",
"en",
"base_model:AstroMLab/astrollama-3-8b-chat_aic",
"base_model:quantized:AstroMLab/astrollama-3-8b-chat_aic",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-09-30T11:36:04" | ---
base_model: AstroMLab/astrollama-3-8b-chat_aic
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- llama-3
- astronomy
- astrophysics
- arxiv
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/AstroMLab/astrollama-3-8b-chat_aic
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/astrollama-3-8b-chat_aic-i1-GGUF/resolve/main/astrollama-3-8b-chat_aic.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
theoracle/autotrain-kaggle | theoracle | "2024-03-29T20:16:16" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"kaggle-qa",
"text-generation",
"peft",
"conversational",
"dataset:custom",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-29T09:49:18" | ---
title: Kaggle Q&A Gemma Model
tags:
- autotrain
- kaggle-qa
- text-generation
- peft
datasets:
- custom
library_name: transformers
widget:
- messages:
- role: user
content: How do I submit to a Kaggle competition?
license: other
---
## Overview
Developed with the cutting-edge AutoTrain and PEFT technologies, this model is specifically trained to provide detailed answers to questions about Kaggle. Whether you're wondering how to get started, how to submit to a competition, or how to navigate the datasets, this model is equipped to assist.
## Key Features
- **Kaggle-Specific Knowledge**: Designed to offer insights and guidance on using Kaggle, from competition submissions to data exploration.
- **Powered by AutoTrain**: Utilizes Hugging Face's AutoTrain for efficient and effective training, ensuring high-quality responses.
- **PEFT Enhanced**: Benefits from PEFT for improved performance and efficiency, making it highly scalable and robust.
## Usage
The following Python code snippet illustrates how to use this model to answer your Kaggle-related questions:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "theoracle/autotrain-kaggle"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
tokenizer.pad_token = tokenizer.eos_token
prompt = '''
### How do I prepare for Kaggle competitions?\n ### Answer:
'''
encoding = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True, max_length=500, add_special_tokens=True)
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']
output_ids = model.generate(
input_ids.to('cuda'),
attention_mask=attention_mask.to('cuda'),
max_new_tokens=300,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(response)
```
## Application Scenarios
This model is particularly useful for:
- Kaggle competitors seeking advice on strategy and submissions.
- Educators and students looking for a tool to facilitate learning through Kaggle competitions.
- Data scientists requiring quick access to information about Kaggle datasets and competitions.
## About AutoTrain and PEFT
AutoTrain by Hugging Face streamlines the model training process, making it easier and more efficient to develop state-of-the-art models. PEFT enhances this by providing a framework for efficient model training and deployment. Together, they enable this model to deliver fast and accurate responses to your Kaggle inquiries.
## License
This model is distributed under an "other" license, allowing diverse applications while encouraging users to review the license terms for compliance with their project requirements. |
ron5569/lamma_7b_test_perf | ron5569 | "2023-11-05T14:27:27" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-11-05T14:18:19" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
|
great0001/7d81fc6a-cb85-4191-8616-1d619cd8f0e3 | great0001 | "2025-01-19T05:23:46" | 7 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Capybara-7B-V1",
"base_model:adapter:NousResearch/Nous-Capybara-7B-V1",
"license:mit",
"region:us"
] | null | "2025-01-19T05:21:18" | ---
library_name: peft
license: mit
base_model: NousResearch/Nous-Capybara-7B-V1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7d81fc6a-cb85-4191-8616-1d619cd8f0e3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Nous-Capybara-7B-V1
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 780a12a658d8c0ff_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/780a12a658d8c0ff_train_data.json
type:
field_instruction: prompt
field_output: good_res
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: great0001/7d81fc6a-cb85-4191-8616-1d619cd8f0e3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/780a12a658d8c0ff_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e1187ce8-ef5e-4551-b9e9-17e5e13814ab
wandb_project: Birthday-SN56-14-Gradients-On-Demand
wandb_run: your_name
wandb_runid: e1187ce8-ef5e-4551-b9e9-17e5e13814ab
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 7d81fc6a-cb85-4191-8616-1d619cd8f0e3
This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0006 | 1 | nan |
| 0.0 | 0.0018 | 3 | nan |
| 0.0 | 0.0036 | 6 | nan |
| 0.0 | 0.0053 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
yfarm01/sn29_dec22_c1 | yfarm01 | "2024-11-22T12:50:58" | 36 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-22T12:48:20" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
maulairfani/autocomplete_gpt2 | maulairfani | "2023-10-05T09:00:37" | 147 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-10-05T08:59:41" | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: autocomplete_gpt2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# autocomplete_gpt2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Tokenizers 0.14.0
|
TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF | TinyUD | "2025-01-17T07:43:54" | 23 | 0 | transformers | [
"transformers",
"gguf",
"conversational",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"ko",
"base_model:rtzr/ko-gemma-2-9b-it",
"base_model:quantized:rtzr/ko-gemma-2-9b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"imatrix"
] | text-generation | "2025-01-17T07:43:31" | ---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
tags:
- conversational
- llama-cpp
- gguf-my-repo
base_model: rtzr/ko-gemma-2-9b-it
language:
- ko
---
# TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF
This model was converted to GGUF format from [`rtzr/ko-gemma-2-9b-it`](https://huggingface.co/rtzr/ko-gemma-2-9b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/rtzr/ko-gemma-2-9b-it) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF --hf-file ko-gemma-2-9b-it-iq3_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF --hf-file ko-gemma-2-9b-it-iq3_m-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF --hf-file ko-gemma-2-9b-it-iq3_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo TinyUD/ko-gemma-2-9b-it-IQ3_M-GGUF --hf-file ko-gemma-2-9b-it-iq3_m-imat.gguf -c 2048
```
|
Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF | Triangle104 | "2025-02-04T07:42:27" | 23 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Statuo/Deepseeker-Kunou-Qwen2.5-14b",
"base_model:quantized:Statuo/Deepseeker-Kunou-Qwen2.5-14b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-04T07:41:44" | ---
base_model: Statuo/Deepseeker-Kunou-Qwen2.5-14b
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
license: apache-2.0
---
# Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF
This model was converted to GGUF format from [`Statuo/Deepseeker-Kunou-Qwen2.5-14b`](https://huggingface.co/Statuo/Deepseeker-Kunou-Qwen2.5-14b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Statuo/Deepseeker-Kunou-Qwen2.5-14b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF --hf-file deepseeker-kunou-qwen2.5-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF --hf-file deepseeker-kunou-qwen2.5-14b-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF --hf-file deepseeker-kunou-qwen2.5-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Deepseeker-Kunou-Qwen2.5-14b-Q4_K_S-GGUF --hf-file deepseeker-kunou-qwen2.5-14b-q4_k_s.gguf -c 2048
```
|
zJuu/Qwen-Qwen2-0.5B-1719269708 | zJuu | "2024-06-24T22:55:10" | 5 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2-0.5B",
"base_model:adapter:Qwen/Qwen2-0.5B",
"region:us"
] | null | "2024-06-24T22:55:08" | ---
library_name: peft
base_model: Qwen/Qwen2-0.5B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
gokuls/distilbert_sa_GLUE_Experiment_logit_kd_wnli_256 | gokuls | "2023-01-30T02:17:26" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-01-30T02:16:36" | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_sa_GLUE_Experiment_logit_kd_wnli_256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE WNLI
type: glue
config: wnli
split: validation
args: wnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5633802816901409
---
<!-- 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_sa_GLUE_Experiment_logit_kd_wnli_256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE WNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3436
- Accuracy: 0.5634
## 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: 256
- eval_batch_size: 256
- seed: 10
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3511 | 1.0 | 3 | 0.3436 | 0.5634 |
| 0.3479 | 2.0 | 6 | 0.3457 | 0.5634 |
| 0.3474 | 3.0 | 9 | 0.3462 | 0.5634 |
| 0.3477 | 4.0 | 12 | 0.3442 | 0.5634 |
| 0.3486 | 5.0 | 15 | 0.3442 | 0.5634 |
| 0.3479 | 6.0 | 18 | 0.3455 | 0.5634 |
### Framework versions
- Transformers 4.26.0
- Pytorch 1.14.0a0+410ce96
- Datasets 2.9.0
- Tokenizers 0.13.2
|
davidschulte/ESM_hope_edi_tamil | davidschulte | "2025-03-26T14:34:02" | 16 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:dravidianlangtech/hope_edi",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-12-05T17:10:06" | ---
base_model: bert-base-multilingual-uncased
datasets:
- dravidianlangtech/hope_edi
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM dravidianlangtech/hope_edi
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** dravidianlangtech/hope_edi
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** dravidianlangtech/hope_edi
- **Subset [optional]:** tamil
- **Text Column:** text
- **Label Column:** label
- **Dataset Split:** train
- **Sample size [optional]:** 10000
- **Sample seed [optional]:** 42
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
LarryAIDraw/kirika_towa_alma_v1 | LarryAIDraw | "2023-05-13T18:26:08" | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-05-13T18:15:14" | ---
license: creativeml-openrail-m
---
https://civitai.com/models/64069/kirika-towa-alma-shining-resonance |
bartowski/Athene-V2-Agent-GGUF | bartowski | "2024-11-15T02:52:52" | 411 | 6 | null | [
"gguf",
"RLHF",
"Nexusflow",
"Athene",
"Function Calling",
"Agent",
"Extraction",
"text-generation",
"en",
"base_model:Nexusflow/Athene-V2-Agent",
"base_model:quantized:Nexusflow/Athene-V2-Agent",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | "2024-11-14T16:28:58" | ---
quantized_by: bartowski
pipeline_tag: text-generation
language:
- en
tags:
- RLHF
- Nexusflow
- Athene
- Function Calling
- Agent
- Extraction
base_model: Nexusflow/Athene-V2-Agent
license: other
---
## Llamacpp imatrix Quantizations of Athene-V2-Agent
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4058">b4058</a> for quantization.
Original model: https://huggingface.co/Nexusflow/Athene-V2-Agent
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Athene-V2-Agent-Q8_0.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q8_0) | Q8_0 | 77.26GB | true | Extremely high quality, generally unneeded but max available quant. |
| [Athene-V2-Agent-Q6_K_L.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q6_K_L) | Q6_K_L | 64.94GB | true | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Athene-V2-Agent-Q6_K.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q6_K) | Q6_K | 64.34GB | true | Very high quality, near perfect, *recommended*. |
| [Athene-V2-Agent-Q5_K_L.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q5_K_L) | Q5_K_L | 55.21GB | true | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Athene-V2-Agent-Q5_K_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q5_K_M) | Q5_K_M | 54.44GB | true | High quality, *recommended*. |
| [Athene-V2-Agent-Q5_K_S.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/tree/main/Athene-V2-Agent-Q5_K_S) | Q5_K_S | 51.37GB | true | High quality, *recommended*. |
| [Athene-V2-Agent-Q4_K_L.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q4_K_L.gguf) | Q4_K_L | 48.33GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Athene-V2-Agent-Q4_K_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q4_K_M.gguf) | Q4_K_M | 47.41GB | false | Good quality, default size for most use cases, *recommended*. |
| [Athene-V2-Agent-Q4_K_S.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q4_K_S.gguf) | Q4_K_S | 43.88GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Athene-V2-Agent-Q4_0.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q4_0.gguf) | Q4_0 | 41.38GB | false | Legacy format, generally not worth using over similarly sized formats |
| [Athene-V2-Agent-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q4_0_8_8.gguf) | Q4_0_8_8 | 41.23GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [Athene-V2-Agent-Q3_K_XL.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q3_K_XL.gguf) | Q3_K_XL | 40.59GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Athene-V2-Agent-IQ4_XS.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ4_XS.gguf) | IQ4_XS | 39.70GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Athene-V2-Agent-Q3_K_L.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q3_K_L.gguf) | Q3_K_L | 39.50GB | false | Lower quality but usable, good for low RAM availability. |
| [Athene-V2-Agent-Q3_K_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q3_K_M.gguf) | Q3_K_M | 37.69GB | false | Low quality. |
| [Athene-V2-Agent-IQ3_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ3_M.gguf) | IQ3_M | 35.50GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Athene-V2-Agent-Q3_K_S.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q3_K_S.gguf) | Q3_K_S | 34.48GB | false | Low quality, not recommended. |
| [Athene-V2-Agent-IQ3_XS.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ3_XS.gguf) | IQ3_XS | 32.84GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Athene-V2-Agent-Q2_K_L.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q2_K_L.gguf) | Q2_K_L | 31.02GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Athene-V2-Agent-Q2_K.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-Q2_K.gguf) | Q2_K | 29.81GB | false | Very low quality but surprisingly usable. |
| [Athene-V2-Agent-IQ2_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ2_M.gguf) | IQ2_M | 29.34GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Athene-V2-Agent-IQ2_S.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ2_S.gguf) | IQ2_S | 27.94GB | false | Low quality, uses SOTA techniques to be usable. |
| [Athene-V2-Agent-IQ2_XS.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ2_XS.gguf) | IQ2_XS | 27.05GB | false | Low quality, uses SOTA techniques to be usable. |
| [Athene-V2-Agent-IQ2_XXS.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ2_XXS.gguf) | IQ2_XXS | 25.49GB | false | Very low quality, uses SOTA techniques to be usable. |
| [Athene-V2-Agent-IQ1_M.gguf](https://huggingface.co/bartowski/Athene-V2-Agent-GGUF/blob/main/Athene-V2-Agent-IQ1_M.gguf) | IQ1_M | 23.74GB | false | Extremely low quality, *not* recommended. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Athene-V2-Agent-GGUF --include "Athene-V2-Agent-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Athene-V2-Agent-GGUF --include "Athene-V2-Agent-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (Athene-V2-Agent-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
wisejiyoon/bert-finetuned-ner | wisejiyoon | "2023-12-08T05:48:43" | 8 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:cc-by-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-12-07T08:14:37" | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8597087378640776
- name: Recall
type: recall
value: 0.8941433860652979
- name: F1
type: f1
value: 0.8765880217785844
- name: Accuracy
type: accuracy
value: 0.9760991339759331
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0943
- Precision: 0.8597
- Recall: 0.8941
- F1: 0.8766
- Accuracy: 0.9761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1321 | 1.0 | 1756 | 0.1003 | 0.8010 | 0.8514 | 0.8254 | 0.9687 |
| 0.0654 | 2.0 | 3512 | 0.0927 | 0.8331 | 0.8862 | 0.8588 | 0.9739 |
| 0.0382 | 3.0 | 5268 | 0.0943 | 0.8597 | 0.8941 | 0.8766 | 0.9761 |
### Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
|
Rodo-Sami/196927de-72a3-4a4c-abf9-e082559e4708 | Rodo-Sami | "2025-02-13T04:07:25" | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-14B-Chat",
"base_model:adapter:Qwen/Qwen1.5-14B-Chat",
"license:other",
"region:us"
] | null | "2025-02-13T03:26:40" | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-14B-Chat
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 196927de-72a3-4a4c-abf9-e082559e4708
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.6.0`
```yaml
adapter: lora
base_model: Qwen/Qwen1.5-14B-Chat
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 956404501872e743_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/956404501872e743_train_data.json
type:
field_input: eval_persona
field_instruction: eval_question
field_output: eval_whole_desc
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: Rodo-Sami/196927de-72a3-4a4c-abf9-e082559e4708
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/956404501872e743_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 68e338b5-c920-4bfc-ba14-6ee6beade006
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 68e338b5-c920-4bfc-ba14-6ee6beade006
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 196927de-72a3-4a4c-abf9-e082559e4708
This model is a fine-tuned version of [Qwen/Qwen1.5-14B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_bnb_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3674 | 0.0736 | 50 | 1.5408 |
| 1.2695 | 0.1471 | 100 | 1.4699 |
| 1.2745 | 0.2207 | 150 | 1.4033 |
| 1.2681 | 0.2942 | 200 | 1.3864 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
kenhktsui/fasttext_test | kenhktsui | "2024-08-04T18:09:05" | 11 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-07-30T14:39:16" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## 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]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
shanhy/xlm-roberta-base_seed42_original_amh-esp-eng_train | shanhy | "2024-02-15T00:53:53" | 90 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-15T00:53:13" | ---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_seed42_original_amh-esp-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_seed42_original_amh-esp-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0175
- Spearman Corr: 0.8510
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.76 | 200 | 0.0231 | 0.8199 |
| 0.0378 | 3.52 | 400 | 0.0142 | 0.8523 |
| 0.021 | 5.29 | 600 | 0.0142 | 0.8544 |
| 0.0157 | 7.05 | 800 | 0.0144 | 0.8553 |
| 0.0125 | 8.81 | 1000 | 0.0159 | 0.8538 |
| 0.0104 | 10.57 | 1200 | 0.0156 | 0.8515 |
| 0.0083 | 12.33 | 1400 | 0.0158 | 0.8503 |
| 0.0067 | 14.1 | 1600 | 0.0143 | 0.8510 |
| 0.0067 | 15.86 | 1800 | 0.0183 | 0.8493 |
| 0.0059 | 17.62 | 2000 | 0.0175 | 0.8510 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF | mradermacher | "2025-03-05T11:00:19" | 322 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:PurpleAILAB/Llama-3.1-8B-uncensored_SQLi",
"base_model:quantized:PurpleAILAB/Llama-3.1-8B-uncensored_SQLi",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-01T18:02:11" | ---
base_model: PurpleAILAB/Llama-3.1-8B-uncensored_SQLi
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/PurpleAILAB/Llama-3.1-8B-uncensored_SQLi
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.1-8B-uncensored_SQLi-GGUF/resolve/main/Llama-3.1-8B-uncensored_SQLi.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF | bunnycore | "2024-10-13T19:23:43" | 6 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1",
"base_model:quantized:bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-10-13T19:23:14" | ---
base_model: bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF
This model was converted to GGUF format from [`bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1`](https://huggingface.co/bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF --hf-file llama-3.1-8b-titanfusion-mix-2.1-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF --hf-file llama-3.1-8b-titanfusion-mix-2.1-q4_k_m-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF --hf-file llama-3.1-8b-titanfusion-mix-2.1-q4_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo bunnycore/Llama-3.1-8B-TitanFusion-Mix-2.1-Q4_K_M-GGUF --hf-file llama-3.1-8b-titanfusion-mix-2.1-q4_k_m-imat.gguf -c 2048
```
|
Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF | Triangle104 | "2025-01-14T10:47:20" | 26 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B",
"base_model:quantized:grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-01-14T10:46:51" | ---
base_model: grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B
library_name: transformers
pipeline_tag: text-generation
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
license: llama3.1
---
# Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF
This model was converted to GGUF format from [`grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B`](https://huggingface.co/grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/grimjim/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF --hf-file llama3.1-supernovalite-huatuoskywork-o1-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF --hf-file llama3.1-supernovalite-huatuoskywork-o1-8b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF --hf-file llama3.1-supernovalite-huatuoskywork-o1-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama3.1-SuperNovaLite-HuatuoSkywork-o1-8B-Q6_K-GGUF --hf-file llama3.1-supernovalite-huatuoskywork-o1-8b-q6_k.gguf -c 2048
```
|
lesso17/94469e1a-cdb2-4fd5-968a-881849e6b91d | lesso17 | "2025-03-09T02:40:05" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Maykeye/TinyLLama-v0",
"base_model:adapter:Maykeye/TinyLLama-v0",
"license:apache-2.0",
"region:us"
] | null | "2025-03-09T02:32:04" | ---
library_name: peft
license: apache-2.0
base_model: Maykeye/TinyLLama-v0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 94469e1a-cdb2-4fd5-968a-881849e6b91d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Maykeye/TinyLLama-v0
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 23247f1a767c02a5_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/23247f1a767c02a5_train_data.json
type:
field_input: context
field_instruction: instruction
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso17/94469e1a-cdb2-4fd5-968a-881849e6b91d
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000217
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/23247f1a767c02a5_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 170
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 6a5e844d-6526-4037-938e-5e00a4fd1f37
wandb_project: 17a
wandb_run: your_name
wandb_runid: 6a5e844d-6526-4037-938e-5e00a4fd1f37
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 94469e1a-cdb2-4fd5-968a-881849e6b91d
This model is a fine-tuned version of [Maykeye/TinyLLama-v0](https://huggingface.co/Maykeye/TinyLLama-v0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.5584
## 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: 0.000217
- train_batch_size: 4
- eval_batch_size: 4
- seed: 170
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0012 | 1 | 10.3653 |
| 7.5451 | 0.5823 | 500 | 7.5584 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
RayLandNika/Belle_RVC_2 | RayLandNika | "2024-05-31T07:32:01" | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | "2024-05-31T07:32:01" | ---
license: bigscience-openrail-m
---
|
huggingtweets/java_jigga | huggingtweets | "2021-05-22T09:15:54" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05" | ---
language: en
thumbnail: https://www.huggingtweets.com/java_jigga/1617788084385/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div>
<div style="width: 132px; height:132px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1377879160173993987/20XH6CdP_400x400.jpg')">
</div>
<div style="margin-top: 8px; font-size: 19px; font-weight: 800">Cool Narcissist 🤖 AI Bot </div>
<div style="font-size: 15px">@java_jigga bot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on [@java_jigga's tweets](https://twitter.com/java_jigga).
| Data | Quantity |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 313 |
| Short tweets | 426 |
| Tweets kept | 2507 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/kvpyc8u1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @java_jigga's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6p3ishch) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6p3ishch/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/java_jigga')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
PriyankSisodia/bloom_3B_test20ep | PriyankSisodia | "2023-11-20T09:32:35" | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-3b",
"base_model:adapter:bigscience/bloom-3b",
"region:us"
] | null | "2023-11-20T09:32:30" | ---
library_name: peft
base_model: bigscience/bloom-3b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2 | luomingshuang | "2022-07-19T11:56:33" | 0 | 0 | null | [
"region:us"
] | null | "2022-05-16T08:24:41" | Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/355
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/aidatatang_200zh/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless2/train.py \
--world-size 2 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--lang-dir data/lang_char \
--max-duration 250
```
## Evaluation results
The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 5.53 | 6.59 | --epoch 29, --avg 19, --max-duration 100 |
| modified beam search (beam size 4) | 5.28 | 6.32 | --epoch 29, --avg 19, --max-duration 100 |
| fast beam search (set as default) | 5.29 | 6.33 | --epoch 29, --avg 19, --max-duration 1500|
|
emilykang/Phi_medmcqa_question_generation-pharmacology_lora | emilykang | "2024-05-18T18:35:45" | 4 | 0 | peft | [
"peft",
"safetensors",
"phi",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | "2024-05-18T17:16:48" | ---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- generator
model-index:
- name: Phi_medmcqa_question_generation-pharmacology_lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Phi_medmcqa_question_generation-pharmacology_lora
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1 |
WALIDALI/marimrev | WALIDALI | "2023-07-10T05:32:46" | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-07-10T05:29:09" | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### marimrev Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
1231czx/9b_raft_iter1 | 1231czx | "2024-07-14T13:50:09" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-14T13:41:30" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
zon5000/MedLLM_merged-GGUF | zon5000 | "2024-04-06T21:12:11" | 4 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2024-03-31T06:17:04" | ---
license: mit
language:
- en
pipeline_tag: text-generation
--- |
flax-community/arabic-t5-small | flax-community | "2023-11-29T15:17:26" | 44 | 8 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"ar",
"dataset:mc4",
"dataset:oscar",
"dataset:arabic_billion_words",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-03-02T23:29:05" | ---
language:
- ar
datasets:
- mc4
- oscar
- arabic_billion_words
---
# arabic-t5-small
This is a T5v1.1 (small) trained on the concatenation of the Arabic Billion Words corpus and the Arabic subsets of the mC4 and Oscar datasets.
The model could only be trained for about `10%` of the whole dataset due to time limitations. This is equivalent to `22'000` steps or about `4.3` Billion tokens.
## Training parameters
| | |
| :-------------------: | :-----------: |
| Training batch size | `384` |
| Evaluation batch size | `768` |
| learning rate | `1e-2` |
| dtype | `jnp.float32` |
## Preprocessing and the tokenizer
We tried to keep the preprocessing to a bare minimum. We only replaced URLs, emails and social media user mentions with fixed tokens.
Contrary to other pretrained Arabic LMs, we decided to not strip the Arabic diacritics and to keep them part of the vocabulary.
The tokenizer was trained on `5%` of the training set, with a vocabulary size of `64'000`.
For more details about preprocessing, check the [tokenizer code](https://huggingface.co/flax-community/arabic-t5-small/blob/main/t5_tokenizer_model.py)
## Data
The model was trained on the concatenation of the Arabic Billion Words corpus and the Arabic subsets of the mC4 and Oscar datasets.
A random `0.1%` subset of the data was reserved for evaluation and the rest for training.
## Results
| | |
| :-----------------: | :-----------: |
| Evaluation accuracy | `56.84%` |
| Evaluation Loss | `2.423` |
| Training Loss | `2.392` |
| Training Time | `22h 23m 51s` |
## Note for finetuning
This model was pretrained with dropout turned off, so the default `dropout_rate` in the model config is `0`.
To finetune the model dropout should be turned be back on, like this:
```python
model = T5ForConditionalGeneration.from_pretrained("flax-community/arabic-t5-small", dropout_rate=0.1)
```
or,
```python
model = AutoModelForSeq2SeqLM.from_pretrained("flax-community/arabic-t5-small", dropout_rate=0.1)
```
|
hai-minh-son/lstm-attention-nwp-model3 | hai-minh-son | "2025-04-02T05:50:28" | 0 | 0 | null | [
"pytorch",
"region:us"
] | null | "2025-04-01T04:55:57" | # Mô hình LSTM_ATTENTION cho Next Word Prediction
## Thông tin mô hình
- Tên: lstm_attention
- Ngày huấn luyện: 2025-04-02 05:50:16
- Kích thước embedding: 256
- Kích thước hidden: 512
- Số lớp: 2
- Tỉ lệ dropout: 0.3
- Phần trăm dữ liệu sử dụng: 1.0%
- Epochs: 10
- Thiết bị: cuda
- Batch size: 63
## Hiệu suất mô hình
- final_train_loss: 4.7111
- final_val_loss: 4.7266
- final_train_acc: 0.2527
- final_val_acc: 0.2542
|
DevQuasar/nvidia.Minitron-4B-Base-GGUF | DevQuasar | "2025-02-17T17:46:51" | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:nvidia/Minitron-4B-Base",
"base_model:quantized:nvidia/Minitron-4B-Base",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-17T17:17:58" | ---
base_model:
- nvidia/Minitron-4B-Base
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [nvidia/Minitron-4B-Base](https://huggingface.co/nvidia/Minitron-4B-Base)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
tanatapanun/fine-tuned-flan-t5-20-epochs | tanatapanun | "2023-12-28T05:05:46" | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-12-28T04:03:04" | ---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fine-tuned-flan-t5-20-epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# fine-tuned-flan-t5-20-epochs
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7842
- Rouge1: 0.2614
- Rouge2: 0.0824
- Rougel: 0.226
- Rougelsum: 0.2273
- Gen Len: 14.54
## 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: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 301 | 1.8551 | 0.1314 | 0.0425 | 0.1139 | 0.1139 | 11.4 |
| 2.7128 | 2.0 | 602 | 0.9826 | 0.1868 | 0.065 | 0.1564 | 0.1571 | 15.06 |
| 2.7128 | 3.0 | 903 | 0.8569 | 0.2079 | 0.0718 | 0.1716 | 0.1722 | 15.05 |
| 1.1113 | 4.0 | 1204 | 0.8300 | 0.2141 | 0.0705 | 0.181 | 0.181 | 14.59 |
| 0.9116 | 5.0 | 1505 | 0.8204 | 0.2254 | 0.0837 | 0.1943 | 0.1945 | 14.92 |
| 0.9116 | 6.0 | 1806 | 0.8116 | 0.243 | 0.0807 | 0.2074 | 0.2072 | 15.03 |
| 0.8732 | 7.0 | 2107 | 0.8082 | 0.2376 | 0.0752 | 0.2015 | 0.2016 | 14.83 |
| 0.8732 | 8.0 | 2408 | 0.8007 | 0.2345 | 0.0735 | 0.2015 | 0.2021 | 14.41 |
| 0.8336 | 9.0 | 2709 | 0.7968 | 0.2456 | 0.0757 | 0.2081 | 0.2081 | 14.4 |
| 0.8151 | 10.0 | 3010 | 0.7942 | 0.2544 | 0.0752 | 0.2134 | 0.2146 | 14.58 |
| 0.8151 | 11.0 | 3311 | 0.7924 | 0.2497 | 0.0783 | 0.2118 | 0.2124 | 14.5 |
| 0.8187 | 12.0 | 3612 | 0.7907 | 0.2552 | 0.0769 | 0.2189 | 0.2191 | 14.43 |
| 0.8187 | 13.0 | 3913 | 0.7891 | 0.258 | 0.077 | 0.2197 | 0.2199 | 14.37 |
| 0.8028 | 14.0 | 4214 | 0.7867 | 0.2511 | 0.0801 | 0.2146 | 0.2147 | 14.71 |
| 0.7793 | 15.0 | 4515 | 0.7852 | 0.2551 | 0.0777 | 0.2175 | 0.2177 | 14.67 |
| 0.7793 | 16.0 | 4816 | 0.7858 | 0.2594 | 0.0774 | 0.2219 | 0.2219 | 14.47 |
| 0.7872 | 17.0 | 5117 | 0.7850 | 0.2609 | 0.0803 | 0.2233 | 0.2244 | 14.56 |
| 0.7872 | 18.0 | 5418 | 0.7843 | 0.2599 | 0.0811 | 0.2242 | 0.2256 | 14.55 |
| 0.7756 | 19.0 | 5719 | 0.7844 | 0.261 | 0.0824 | 0.2256 | 0.2271 | 14.55 |
| 0.7752 | 20.0 | 6020 | 0.7842 | 0.2614 | 0.0824 | 0.226 | 0.2273 | 14.54 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF | itlwas | "2024-12-29T14:37:50" | 19 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"reasoning",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:KingNish/reasoning-base-20k",
"base_model:KingNish/Reasoning-Llama-1b-v0.1",
"base_model:quantized:KingNish/Reasoning-Llama-1b-v0.1",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-12-29T14:37:42" | ---
base_model: KingNish/Reasoning-Llama-1b-v0.1
datasets:
- KingNish/reasoning-base-20k
language:
- en
license: llama3.2
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
- reasoning
- llama-3
- llama-cpp
- gguf-my-repo
---
# itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF
This model was converted to GGUF format from [`KingNish/Reasoning-Llama-1b-v0.1`](https://huggingface.co/KingNish/Reasoning-Llama-1b-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/KingNish/Reasoning-Llama-1b-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF --hf-file reasoning-llama-1b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF --hf-file reasoning-llama-1b-v0.1-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF --hf-file reasoning-llama-1b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo itlwas/Reasoning-Llama-1b-v0.1-Q4_K_M-GGUF --hf-file reasoning-llama-1b-v0.1-q4_k_m.gguf -c 2048
```
|
Babivill/leidirocha | Babivill | "2023-05-16T09:39:41" | 35 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2022-12-22T19:58:20" | ---
license: creativeml-openrail-m
tags:
- text-to-image
widget:
- text: leidirocha
---
### leidirocha Dreambooth model trained by Babivill with [Hugging Face Dreambooth Training Space](https://huggingface.co/spaces/multimodalart/dreambooth-training) with the v1-5 base model
You run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb). Don't forget to use the concept prompts!
Sample pictures of:
leidirocha (use that on your prompt)
|
utahnlp/snli_facebook_opt-350m_seed-2 | utahnlp | "2024-04-06T02:12:20" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-04-06T02:11:32" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### 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:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
aegunal/FT_IPD_gemma7b | aegunal | "2024-03-11T18:02:53" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-03-11T18:02:50" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
DouglasPontes/2020-Q2-full_tweets_combined90 | DouglasPontes | "2024-01-22T22:10:02" | 19 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-2019-90m",
"base_model:finetune:cardiffnlp/twitter-roberta-base-2019-90m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-01-19T13:28:14" | ---
license: mit
base_model: cardiffnlp/twitter-roberta-base-2019-90m
tags:
- generated_from_trainer
model-index:
- name: 2020-Q2-full_tweets_combined90
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2020-Q2-full_tweets_combined90
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9422
## 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: 4.1e-07
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2400000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-------:|:---------------:|
| No log | 0.02 | 8000 | 2.2220 |
| 2.4154 | 0.03 | 16000 | 2.1427 |
| 2.4154 | 0.05 | 24000 | 2.1028 |
| 2.2273 | 0.07 | 32000 | 2.0824 |
| 2.2273 | 0.08 | 40000 | 2.0645 |
| 2.1774 | 0.1 | 48000 | 2.0478 |
| 2.1774 | 0.12 | 56000 | 2.0327 |
| 2.1569 | 0.13 | 64000 | 2.0248 |
| 2.1569 | 0.15 | 72000 | 2.0209 |
| 2.1439 | 0.17 | 80000 | 2.0049 |
| 2.1439 | 0.19 | 88000 | 2.0113 |
| 2.1271 | 0.2 | 96000 | 2.0038 |
| 2.1271 | 0.22 | 104000 | 2.0065 |
| 2.1211 | 0.24 | 112000 | 1.9987 |
| 2.1211 | 0.25 | 120000 | 1.9929 |
| 2.1194 | 0.27 | 128000 | 1.9922 |
| 2.1194 | 0.29 | 136000 | 1.9917 |
| 2.1118 | 0.3 | 144000 | 1.9885 |
| 2.1118 | 0.32 | 152000 | 1.9870 |
| 2.1047 | 0.34 | 160000 | 1.9843 |
| 2.1047 | 0.35 | 168000 | 1.9827 |
| 2.1015 | 0.37 | 176000 | 1.9826 |
| 2.1015 | 0.39 | 184000 | 1.9774 |
| 2.1042 | 0.4 | 192000 | 1.9771 |
| 2.1042 | 0.42 | 200000 | 1.9770 |
| 2.0919 | 0.44 | 208000 | 1.9752 |
| 2.0919 | 0.45 | 216000 | 1.9775 |
| 2.0953 | 0.47 | 224000 | 1.9684 |
| 2.0953 | 0.49 | 232000 | 1.9748 |
| 2.0848 | 0.51 | 240000 | 1.9714 |
| 2.0848 | 0.52 | 248000 | 1.9781 |
| 2.0882 | 0.54 | 256000 | 1.9709 |
| 2.0882 | 0.56 | 264000 | 1.9660 |
| 2.0922 | 0.57 | 272000 | 1.9651 |
| 2.0922 | 0.59 | 280000 | 1.9678 |
| 2.0938 | 0.61 | 288000 | 1.9667 |
| 2.0938 | 0.62 | 296000 | 1.9630 |
| 2.095 | 0.64 | 304000 | 1.9642 |
| 2.095 | 0.66 | 312000 | 1.9624 |
| 2.0908 | 0.67 | 320000 | 1.9603 |
| 2.0908 | 0.69 | 328000 | 1.9649 |
| 2.0927 | 0.71 | 336000 | 1.9641 |
| 2.0927 | 0.72 | 344000 | 1.9603 |
| 2.0931 | 0.74 | 352000 | 1.9590 |
| 2.0931 | 0.76 | 360000 | 1.9644 |
| 2.087 | 0.77 | 368000 | 1.9635 |
| 2.087 | 0.79 | 376000 | 1.9614 |
| 2.0792 | 0.81 | 384000 | 1.9591 |
| 2.0792 | 0.83 | 392000 | 1.9575 |
| 2.0899 | 0.84 | 400000 | 1.9592 |
| 2.0899 | 0.86 | 408000 | 1.9619 |
| 2.0812 | 0.88 | 416000 | 1.9582 |
| 2.0812 | 0.89 | 424000 | 1.9580 |
| 2.0948 | 0.91 | 432000 | 1.9587 |
| 2.0948 | 0.93 | 440000 | 1.9593 |
| 2.0895 | 0.94 | 448000 | 1.9608 |
| 2.0895 | 0.96 | 456000 | 1.9566 |
| 2.0756 | 0.98 | 464000 | 1.9525 |
| 2.0756 | 0.99 | 472000 | 1.9541 |
| 2.0842 | 1.01 | 480000 | 1.9601 |
| 2.0842 | 1.03 | 488000 | 1.9564 |
| 2.0935 | 1.04 | 496000 | 1.9522 |
| 2.0935 | 1.06 | 504000 | 1.9532 |
| 2.0836 | 1.08 | 512000 | 1.9537 |
| 2.0836 | 1.09 | 520000 | 1.9553 |
| 2.0876 | 1.11 | 528000 | 1.9469 |
| 2.0876 | 1.13 | 536000 | 1.9497 |
| 2.0778 | 1.15 | 544000 | 1.9542 |
| 2.0778 | 1.16 | 552000 | 1.9516 |
| 2.0829 | 1.18 | 560000 | 1.9506 |
| 2.0829 | 1.2 | 568000 | 1.9505 |
| 2.0864 | 1.21 | 576000 | 1.9531 |
| 2.0864 | 1.23 | 584000 | 1.9455 |
| 2.0893 | 1.25 | 592000 | 1.9471 |
| 2.0893 | 1.26 | 600000 | 1.9539 |
| 2.0808 | 1.28 | 608000 | 1.9455 |
| 2.0808 | 1.3 | 616000 | 1.9497 |
| 2.0838 | 1.31 | 624000 | 1.9466 |
| 2.0838 | 1.33 | 632000 | 1.9498 |
| 2.0812 | 1.35 | 640000 | 1.9510 |
| 2.0812 | 1.36 | 648000 | 1.9526 |
| 2.0793 | 1.38 | 656000 | 1.9471 |
| 2.0793 | 1.4 | 664000 | 1.9469 |
| 2.0789 | 1.41 | 672000 | 1.9455 |
| 2.0789 | 1.43 | 680000 | 1.9469 |
| 2.0883 | 1.45 | 688000 | 1.9439 |
| 2.0883 | 1.47 | 696000 | 1.9439 |
| 2.09 | 1.48 | 704000 | 1.9416 |
| 2.09 | 1.5 | 712000 | 1.9492 |
| 2.0845 | 1.52 | 720000 | 1.9430 |
| 2.0845 | 1.53 | 728000 | 1.9484 |
| 2.0742 | 1.55 | 736000 | 1.9456 |
| 2.0742 | 1.57 | 744000 | 1.9380 |
| 2.0839 | 1.58 | 752000 | 1.9418 |
| 2.0839 | 1.6 | 760000 | 1.9434 |
| 2.0806 | 1.62 | 768000 | 1.9450 |
| 2.0806 | 1.63 | 776000 | 1.9426 |
| 2.0805 | 1.65 | 784000 | 1.9441 |
| 2.0805 | 1.67 | 792000 | 1.9459 |
| 2.0833 | 1.68 | 800000 | 1.9435 |
| 2.0833 | 1.7 | 808000 | 1.9455 |
| 2.0763 | 1.72 | 816000 | 1.9421 |
| 2.0763 | 1.73 | 824000 | 1.9438 |
| 2.0758 | 1.75 | 832000 | 1.9371 |
| 2.0758 | 1.77 | 840000 | 1.9432 |
| 2.0888 | 1.79 | 848000 | 1.9414 |
| 2.0888 | 1.8 | 856000 | 1.9444 |
| 2.0786 | 1.82 | 864000 | 1.9408 |
| 2.0786 | 1.84 | 872000 | 1.9397 |
| 2.079 | 1.85 | 880000 | 1.9406 |
| 2.079 | 1.87 | 888000 | 1.9442 |
| 2.0817 | 1.89 | 896000 | 1.9404 |
| 2.0817 | 1.9 | 904000 | 1.9450 |
| 2.0792 | 1.92 | 912000 | 1.9380 |
| 2.0792 | 1.94 | 920000 | 1.9385 |
| 2.0741 | 1.95 | 928000 | 1.9449 |
| 2.0741 | 1.97 | 936000 | 1.9414 |
| 2.0832 | 1.99 | 944000 | 1.9402 |
| 2.0832 | 2.0 | 952000 | 1.9410 |
| 2.0695 | 2.02 | 960000 | 1.9371 |
| 2.0695 | 2.04 | 968000 | 1.9342 |
| 2.0813 | 2.05 | 976000 | 1.9376 |
| 2.0813 | 2.07 | 984000 | 1.9397 |
| 2.0804 | 2.09 | 992000 | 1.9394 |
| 2.0804 | 2.11 | 1000000 | 1.9370 |
| 2.0789 | 2.12 | 1008000 | 1.9350 |
| 2.0789 | 2.14 | 1016000 | 1.9327 |
| 2.0754 | 2.16 | 1024000 | 1.9421 |
| 2.0754 | 2.17 | 1032000 | 1.9371 |
| 2.0774 | 2.19 | 1040000 | 1.9411 |
| 2.0774 | 2.21 | 1048000 | 1.9337 |
| 2.0766 | 2.22 | 1056000 | 1.9387 |
| 2.0766 | 2.24 | 1064000 | 1.9334 |
| 2.079 | 2.26 | 1072000 | 1.9386 |
| 2.079 | 2.27 | 1080000 | 1.9335 |
| 2.068 | 2.29 | 1088000 | 1.9363 |
| 2.068 | 2.31 | 1096000 | 1.9420 |
| 2.0786 | 2.32 | 1104000 | 1.9331 |
| 2.0786 | 2.34 | 1112000 | 1.9327 |
| 2.0734 | 2.36 | 1120000 | 1.9391 |
| 2.0734 | 2.37 | 1128000 | 1.9363 |
| 2.0787 | 2.39 | 1136000 | 1.9321 |
| 2.0787 | 2.41 | 1144000 | 1.9333 |
| 2.0731 | 2.43 | 1152000 | 1.9369 |
| 2.0731 | 2.44 | 1160000 | 1.9357 |
| 2.0816 | 2.46 | 1168000 | 1.9353 |
| 2.0816 | 2.48 | 1176000 | 1.9319 |
| 2.0758 | 2.49 | 1184000 | 1.9366 |
| 2.0758 | 2.51 | 1192000 | 1.9301 |
| 2.0725 | 2.53 | 1200000 | 1.9329 |
| 2.0725 | 2.54 | 1208000 | 1.9370 |
| 2.085 | 2.56 | 1216000 | 1.9251 |
| 2.085 | 2.58 | 1224000 | 1.9369 |
| 2.0809 | 2.59 | 1232000 | 1.9377 |
| 2.0809 | 2.61 | 1240000 | 1.9398 |
| 2.0742 | 2.63 | 1248000 | 1.9368 |
| 2.0742 | 2.64 | 1256000 | 1.9389 |
| 2.0743 | 2.66 | 1264000 | 1.9287 |
| 2.0743 | 2.68 | 1272000 | 1.9337 |
| 2.0822 | 2.69 | 1280000 | 1.9323 |
| 2.0822 | 2.71 | 1288000 | 1.9348 |
| 2.0845 | 2.73 | 1296000 | 1.9328 |
| 2.0845 | 2.75 | 1304000 | 1.9324 |
| 2.0706 | 2.76 | 1312000 | 1.9304 |
| 2.0706 | 2.78 | 1320000 | 1.9322 |
| 2.0813 | 2.8 | 1328000 | 1.9320 |
| 2.0813 | 2.81 | 1336000 | 1.9379 |
| 2.0768 | 2.83 | 1344000 | 1.9283 |
| 2.0768 | 2.85 | 1352000 | 1.9352 |
| 2.0776 | 2.86 | 1360000 | 1.9266 |
| 2.0776 | 2.88 | 1368000 | 1.9339 |
| 2.0776 | 2.9 | 1376000 | 1.9371 |
| 2.0776 | 2.91 | 1384000 | 1.9353 |
| 2.072 | 2.93 | 1392000 | 1.9290 |
| 2.072 | 2.95 | 1400000 | 1.9337 |
| 2.077 | 2.96 | 1408000 | 1.9318 |
| 2.077 | 2.98 | 1416000 | 1.9326 |
| 2.0777 | 3.0 | 1424000 | 1.9338 |
| 2.0777 | 3.01 | 1432000 | 1.9307 |
| 2.0846 | 3.03 | 1440000 | 1.9305 |
| 2.0846 | 3.05 | 1448000 | 1.9312 |
| 2.0744 | 3.07 | 1456000 | 1.9332 |
| 2.0744 | 3.08 | 1464000 | 1.9313 |
| 2.0767 | 3.1 | 1472000 | 1.9311 |
| 2.0767 | 3.12 | 1480000 | 1.9322 |
| 2.082 | 3.13 | 1488000 | 1.9362 |
| 2.082 | 3.15 | 1496000 | 1.9329 |
| 2.0774 | 3.17 | 1504000 | 1.9335 |
| 2.0774 | 3.18 | 1512000 | 1.9342 |
| 2.0793 | 3.2 | 1520000 | 1.9326 |
| 2.0793 | 3.22 | 1528000 | 1.9313 |
| 2.0834 | 3.23 | 1536000 | 1.9302 |
| 2.0834 | 3.25 | 1544000 | 1.9299 |
| 2.0698 | 3.27 | 1552000 | 1.9288 |
| 2.0698 | 3.28 | 1560000 | 1.9311 |
| 2.0721 | 3.3 | 1568000 | 1.9262 |
| 2.0721 | 3.32 | 1576000 | 1.9320 |
| 2.0742 | 3.33 | 1584000 | 1.9278 |
| 2.0742 | 3.35 | 1592000 | 1.9333 |
| 2.0774 | 3.37 | 1600000 | 1.9252 |
| 2.0774 | 3.39 | 1608000 | 1.9301 |
| 2.0766 | 3.4 | 1616000 | 1.9344 |
| 2.0766 | 3.42 | 1624000 | 1.9320 |
| 2.0702 | 3.44 | 1632000 | 1.9307 |
| 2.0702 | 3.45 | 1640000 | 1.9304 |
| 2.0772 | 3.47 | 1648000 | 1.9280 |
| 2.0772 | 3.49 | 1656000 | 1.9324 |
| 2.0757 | 3.5 | 1664000 | 1.9343 |
| 2.0757 | 3.52 | 1672000 | 1.9312 |
| 2.0747 | 3.54 | 1680000 | 1.9304 |
| 2.0747 | 3.55 | 1688000 | 1.9360 |
| 2.068 | 3.57 | 1696000 | 1.9297 |
| 2.068 | 3.59 | 1704000 | 1.9337 |
| 2.0825 | 3.6 | 1712000 | 1.9293 |
| 2.0825 | 3.62 | 1720000 | 1.9295 |
| 2.0811 | 3.64 | 1728000 | 1.9315 |
| 2.0811 | 3.65 | 1736000 | 1.9279 |
| 2.0844 | 3.67 | 1744000 | 1.9289 |
| 2.0844 | 3.69 | 1752000 | 1.9279 |
| 2.0827 | 3.71 | 1760000 | 1.9283 |
| 2.0827 | 3.72 | 1768000 | 1.9295 |
| 2.0684 | 3.74 | 1776000 | 1.9281 |
| 2.0684 | 3.76 | 1784000 | 1.9330 |
| 2.0724 | 3.77 | 1792000 | 1.9294 |
| 2.0724 | 3.79 | 1800000 | 1.9276 |
| 2.074 | 3.81 | 1808000 | 1.9227 |
| 2.074 | 3.82 | 1816000 | 1.9320 |
| 2.0801 | 3.84 | 1824000 | 1.9275 |
| 2.0801 | 3.86 | 1832000 | 1.9302 |
| 2.0783 | 3.87 | 1840000 | 1.9333 |
| 2.0783 | 3.89 | 1848000 | 1.9296 |
| 2.0787 | 3.91 | 1856000 | 1.9302 |
| 2.0787 | 3.92 | 1864000 | 1.9347 |
| 2.0733 | 3.94 | 1872000 | 1.9298 |
| 2.0733 | 3.96 | 1880000 | 1.9302 |
| 2.0742 | 3.97 | 1888000 | 1.9279 |
| 2.0742 | 3.99 | 1896000 | 1.9258 |
| 2.0769 | 4.01 | 1904000 | 1.9255 |
| 2.0769 | 4.03 | 1912000 | 1.9282 |
| 2.0736 | 4.04 | 1920000 | 1.9298 |
| 2.0736 | 4.06 | 1928000 | 1.9325 |
| 2.0713 | 4.08 | 1936000 | 1.9296 |
| 2.0713 | 4.09 | 1944000 | 1.9293 |
| 2.0825 | 4.11 | 1952000 | 1.9345 |
| 2.0825 | 4.13 | 1960000 | 1.9346 |
| 2.0828 | 4.14 | 1968000 | 1.9311 |
| 2.0828 | 4.16 | 1976000 | 1.9307 |
| 2.0821 | 4.18 | 1984000 | 1.9336 |
| 2.0821 | 4.19 | 1992000 | 1.9265 |
| 2.0768 | 4.21 | 2000000 | 1.9284 |
| 2.0768 | 4.23 | 2008000 | 1.9290 |
| 2.0695 | 4.24 | 2016000 | 1.9306 |
| 2.0695 | 4.26 | 2024000 | 1.9299 |
| 2.0698 | 4.28 | 2032000 | 1.9230 |
| 2.0698 | 4.29 | 2040000 | 1.9272 |
| 2.0776 | 4.31 | 2048000 | 1.9306 |
| 2.0776 | 4.33 | 2056000 | 1.9243 |
| 2.0797 | 4.35 | 2064000 | 1.9266 |
| 2.0797 | 4.36 | 2072000 | 1.9249 |
| 2.0808 | 4.38 | 2080000 | 1.9279 |
| 2.0808 | 4.4 | 2088000 | 1.9262 |
| 2.0776 | 4.41 | 2096000 | 1.9350 |
| 2.0776 | 4.43 | 2104000 | 1.9297 |
| 2.0805 | 4.45 | 2112000 | 1.9337 |
| 2.0805 | 4.46 | 2120000 | 1.9302 |
| 2.0791 | 4.48 | 2128000 | 1.9337 |
| 2.0791 | 4.5 | 2136000 | 1.9298 |
| 2.0771 | 4.51 | 2144000 | 1.9268 |
| 2.0771 | 4.53 | 2152000 | 1.9370 |
| 2.0807 | 4.55 | 2160000 | 1.9307 |
| 2.0807 | 4.56 | 2168000 | 1.9292 |
| 2.0856 | 4.58 | 2176000 | 1.9300 |
| 2.0856 | 4.6 | 2184000 | 1.9329 |
| 2.0744 | 4.61 | 2192000 | 1.9319 |
| 2.0744 | 4.63 | 2200000 | 1.9352 |
| 2.0839 | 4.65 | 2208000 | 1.9368 |
| 2.0839 | 4.67 | 2216000 | 1.9343 |
| 2.0706 | 4.68 | 2224000 | 1.9290 |
| 2.0706 | 4.7 | 2232000 | 1.9347 |
| 2.0745 | 4.72 | 2240000 | 1.9294 |
| 2.0745 | 4.73 | 2248000 | 1.9255 |
| 2.0767 | 4.75 | 2256000 | 1.9271 |
| 2.0767 | 4.77 | 2264000 | 1.9296 |
| 2.0753 | 4.78 | 2272000 | 1.9268 |
| 2.0753 | 4.8 | 2280000 | 1.9292 |
| 2.0716 | 4.82 | 2288000 | 1.9310 |
| 2.0716 | 4.83 | 2296000 | 1.9267 |
| 2.0778 | 4.85 | 2304000 | 1.9301 |
| 2.0778 | 4.87 | 2312000 | 1.9280 |
| 2.0724 | 4.88 | 2320000 | 1.9283 |
| 2.0724 | 4.9 | 2328000 | 1.9289 |
| 2.0811 | 4.92 | 2336000 | 1.9315 |
| 2.0811 | 4.93 | 2344000 | 1.9268 |
| 2.0816 | 4.95 | 2352000 | 1.9304 |
| 2.0816 | 4.97 | 2360000 | 1.9302 |
| 2.0775 | 4.99 | 2368000 | 1.9292 |
| 2.0775 | 5.0 | 2376000 | 1.9274 |
| 2.0807 | 5.02 | 2384000 | 1.9317 |
| 2.0807 | 5.04 | 2392000 | 1.9298 |
| 2.0668 | 5.05 | 2400000 | 1.9349 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|
KingKazma/xsum_gpt2_p_tuning_500_10_3000_8_e1_s108_v4_l4_v100 | KingKazma | "2023-08-13T15:46:55" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-08-13T15:46:54" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0.dev0
|
CyantifiCQ/ppo-Huggy_01 | CyantifiCQ | "2022-12-25T17:46:34" | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2022-12-25T17:46:24" |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
library_name: ml-agents
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Huggy
2. Step 1: Write your model_id: CyantifiCQ/ppo-Huggy_01
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
camilomj/ROSEDEBUT | camilomj | "2024-05-16T23:22:56" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-05-16T23:22:05" | ---
license: apache-2.0
---
|
End of preview. Expand
in Data Studio
Dataset Card for Hugging Face Hub Model Cards
This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
- Downloads last month
- 1,447
Curated by:
Daniel van Strien
Language(s) (NLP):
Model cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
1.03 GB
Size of the auto-converted Parquet files:
1.03 GB
Number of rows:
703,608