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transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/rafaeloc15/Beyondrisk-Llama3-8B-FT
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Beyondrisk-Llama3-8B-FT-GGUF/resolve/main/Beyondrisk-Llama3-8B-FT.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 -->
|
{"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "rafaeloc15/Beyondrisk-Llama3-8B-FT", "quantized_by": "mradermacher"}
|
mradermacher/Beyondrisk-Llama3-8B-FT-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:rafaeloc15/Beyondrisk-Llama3-8B-FT",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:17:20+00:00
|
text-generation
|
transformers
|
# Merged-Vicuna-RP-Stew-34B
Quantized 4.65 exl2 of the model down below:
https://huggingface.co/MarinaraSpaghetti/RP-Stew-v2.5-34B
Specialized parquet used:
https://huggingface.co/datasets/ParasiticRogue/Bluemoon-Light?not-for-all-audiences=true
## Merge Details
It's like RP Stew V2, but slightly different. Joint venture between me and MarinaraSpaghetti in trying to get context slightly longer in reach, while also lowering the flowery prose a tad that some users seemed to of had a problem with. Main difference? Just swapped Nontoxic-PiVoT-Bagel and Nyakura-CausalLM-RP's percentages in the recipe.
### Settings
Temperature @ 0.8
Min-P @ 0.01
Typical-P @ 0.95
Repetition Penalty @ 1.07
Repetition Range @ 4096
Smoothing Factor @ 0.3
Everything else @ off
Early Stopping = X
Do Sample = ✓
Add BOS Token = X
Ban EOS Token = ✓
Skip Special Tokens = X
Temperature Last = ✓
Custom Stopping Strings: "<|im_end|>", "< / s >" (<---without spaces)
### Prompt Format: Chat-Vicuna
```
SYSTEM:
{system_prompt}<|im_end|>
USER:
{prompt}<|im_end|>
ASSISTANT:
{output}<|im_end|>
```
### Models Merged
The following models were included in the merge:
https://huggingface.co/NousResearch/Nous-Capybara-34B
https://huggingface.co/migtissera/Tess-34B-v1.5b
https://huggingface.co/jondurbin/nontoxic-bagel-34b-v0.2
https://huggingface.co/maywell/PiVoT-SUS-RP
https://huggingface.co/Sao10K/NyakuraV2-34B-Yi-Llama
https://huggingface.co/NeverSleep/CausalLM-RP-34B
https://huggingface.co/chargoddard/Yi-34B-200K-Llama
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Nontoxic-PiVoT-Bagel-RP-34b
parameters:
weight: 0.16
density: 0.42
- model: Nyakura-CausalLM-RP-34B
parameters:
weight: 0.22
density: 0.54
- model: Tess-34B-v1.5b
parameters:
weight: 0.28
density: 0.66
- model: Nous-Capybara-34B-V1.9
parameters:
weight: 0.34
density: 0.78
merge_method: dare_ties
base_model: Yi-34B-200K-Llama
parameters:
int8_mask: true
dtype: bfloat16
```
|
{"license": "other", "tags": ["merge", "roleplay", "exl2", "not-for-all-audiences"], "license_name": "yi-34b", "license_link": "https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE"}
|
ParasiticRogue/RP-Stew-v2.5-34B-exl2-4.65
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"roleplay",
"exl2",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:17:39+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Epiculous/Crunchy-onion
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Crunchy-onion-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/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ1_S.gguf) | i1-IQ1_S | 9.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ1_M.gguf) | i1-IQ1_M | 10.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.7 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ2_S.gguf) | i1-IQ2_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ2_M.gguf) | i1-IQ2_M | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/Crunchy-onion-i1-GGUF/resolve/main/Crunchy-onion.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "agpl-3.0", "library_name": "transformers", "datasets": ["lemonilia/LimaRP", "grimulkan/theory-of-mind", "Epiculous/Gnosis"], "base_model": "Epiculous/Crunchy-onion", "quantized_by": "mradermacher"}
|
mradermacher/Crunchy-onion-i1-GGUF
| null |
[
"transformers",
"gguf",
"en",
"dataset:lemonilia/LimaRP",
"dataset:grimulkan/theory-of-mind",
"dataset:Epiculous/Gnosis",
"base_model:Epiculous/Crunchy-onion",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:17:46+00:00
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
AI4DS/CodeLlama-ColSel-33B
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:18:52+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-4
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-4", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:19:59+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-3
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-3", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-31m_mz-130_PasswordMatch_n-its-10-seed-3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:20:12+00:00
|
null | null |
{}
|
Myriam123/wav2vec2_large_1.2
| null |
[
"region:us"
] | null |
2024-04-24T04:23:24+00:00
|
|
null | null |
{}
|
Myriam123/wav2vec2_large_1.2_
| null |
[
"region:us"
] | null |
2024-04-24T04:23:44+00:00
|
|
text-generation
|
transformers
|
{"license": "mit"}
|
migueldeguzmandev/GPT2XL_RLLMv18-3
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:23:45+00:00
|
|
text-generation
|
transformers
|
# RedPajama-INCITE-7B-Base
RedPajama-INCITE-7B-Base was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
The training was done on 3,072 V100 GPUs provided as part of the INCITE 2023 project on Scalable Foundation Models for Transferrable Generalist AI, awarded to MILA, LAION, and EleutherAI in fall 2022, with support from the Oak Ridge Leadership Computing Facility (OLCF) and INCITE program.
- Base Model: [RedPajama-INCITE-7B-Base](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base)
- Instruction-tuned Version: [RedPajama-INCITE-7B-Instruct](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Instruct)
- Chat Version: [RedPajama-INCITE-7B-Chat](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat)
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 6.9B parameter pretrained language model.
# Quick Start
Please note that the model requires `transformers` version >= 4.25.1.
## GPU Inference
This requires a GPU with 16GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
widely considered to be the father of modern computer science and artificial intelligence. He was a brilliant mathematician and cryptographer, who worked for the British government during World War II. He was instrumental in breaking the German Enigma code, and is credited with helping to shorten the war by two years...
"""
```
## GPU Inference in Int8
This requires a GPU with 12GB memory.
To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
```bash
pip install accelerate
pip install bitsandbytes
```
Then you can run inference with int8 as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
a very well-known name in the world of computer science. It is named after the mathematician Alan Turing. He is famous for his work on the Enigma machine, which was used by the Germans during World War II....
"""```
## CPU Inference
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Base", torch_dtype=torch.bfloat16)
# infer
prompt = "Alan Turing is"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
one of the most important figures in the history of computing. He is best known for his work on the development of the modern computer and for his code-breaking work during World War II. He was also a brilliant mathematician and philosopher.
"""
```
Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
# Uses
## Direct Use
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
`RedPajama-INCITE-7B-Base` is a language model and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
`RedPajama-INCITE-7B-Base` is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
`RedPajama-INCITE-7B-Base`, like other language models, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
**Training Procedure**
- **Hardware:** 512 nodes of 6xV100 (IBM Power9), on the OLCF Summit cluster
- **Optimizer:** Apex FusedAdam
- **Parallelism:** Pipeline parallel 12, tensor parallel 2
- **Gradient Accumulations**: 8 (global batch size 4M tokens)
- **Num of Tokens:** 1.001T Tokens
- **Learning rate:** 0.00012
## Benchmark
Please refer to our [blog post](https://together.xyz) for benchmark results.
## Intermediate Checkpoints
We provide 11 intermediate checkpoints that have been released for study.
The checkpoints are organized based on the number of tokens they contain, ranging from 240 billion tokens to 1 trillion tokens.
- [240b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/240b_tokens)
- [280b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/280b_tokens)
- [400b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/400b_tokens)
- [440b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/440b_tokens)
- [500b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/500b_tokens)
- [600b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/600b_tokens)
- [700b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/700b_tokens)
- [720b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/720b_tokens)
- [960b_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/960b_tokens)
- [1t_tokens](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/1t_tokens)
- [latest](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/tree/main)
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
{"language": ["en"], "license": "apache-2.0", "datasets": ["togethercomputer/RedPajama-Data-1T"]}
|
titanbot/ct2-int8-redpajama-7b-base
| null |
[
"transformers",
"gpt_neox",
"text-generation",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:23:53+00:00
|
null |
transformers
|
{}
|
vincentyandex/llama3_8b_chunked_novel_q8_0_bs32_step2000
| null |
[
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:24:02+00:00
|
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"}
|
bmehrba/Llama-2-13b-chat-hf-fine-tuned-adapters_Epistemic_Llama13b_0.0_Seed103
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null |
2024-04-24T04:24:05+00:00
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
{"library_name": "peft", "base_model": "meta-llama/Llama-2-13b-chat-hf"}
|
bmehrba/Llama-2-13b-chat-hf-fine-tuned_Epistemic_Llama13b_0.0_Seed103
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null |
2024-04-24T04:24:26+00:00
|
text-generation
|
transformers
|
# llava-v1.5-llama-3-8b-pretrain Model Card
This is a pretrained checkpoint with the MLP connector after LLaVA stage 1, you can use it to instruct tune your multimodal models.
Please follow my reproduced implementation [LLaVA-Llama-3](https://github.com/Victorwz/LLaVA-Llama-3/) for more details on fine-tuning LLaVA model with Llama-3 as the foundatiaon LLM.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
## Architecture
- LLM: llama-3-8b (Frozen)
- Vision-Language Adapter: MLP
- Vision Encoder: CLIP-ViT-L-336px (Frozen)
|
{"datasets": ["liuhaotian/LLaVA-CC3M-Pretrain-595K"], "inference": false}
|
weizhiwang/llava-v1.5-llama-3-8b-pretrain-clip-large-336px
| null |
[
"transformers",
"llava",
"text-generation",
"dataset:liuhaotian/LLaVA-CC3M-Pretrain-595K",
"autotrain_compatible",
"region:us"
] | null |
2024-04-24T04:24:30+00:00
|
null | null |
{}
|
junweiliao/zephyr-7b-sft-qlora
| null |
[
"tensorboard",
"safetensors",
"region:us"
] | null |
2024-04-24T04:24:51+00:00
|
|
null | null |
{}
|
stafdif/Adawong
| null |
[
"region:us"
] | null |
2024-04-24T04:26:44+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": ["unsloth"]}
|
muharamesa/trainMistral
| null |
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:30:41+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
santoshsto/mistral-7b-python-FINETUNED-4bit
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T04:31:36+00:00
|
multiple-choice
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# e_care_Ba1
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6931
- F1: 0.4746
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.704 | 1.0 | 933 | 0.6931 | 0.5002 |
| 0.7017 | 2.0 | 1866 | 0.6931 | 0.4667 |
| 0.6969 | 3.0 | 2799 | 0.6931 | 0.4621 |
| 0.6978 | 4.0 | 3732 | 0.6931 | 0.4622 |
| 0.6986 | 5.0 | 4665 | 0.6931 | 0.4876 |
| 0.6979 | 6.0 | 5598 | 0.6931 | 0.5377 |
| 0.698 | 7.0 | 6531 | 0.6931 | 0.4836 |
| 0.6972 | 8.0 | 7464 | 0.6931 | 0.4732 |
| 0.6956 | 9.0 | 8397 | 0.6931 | 0.4668 |
| 0.6973 | 10.0 | 9330 | 0.6931 | 0.4746 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "FacebookAI/xlm-roberta-large", "model-index": [{"name": "e_care_Ba1", "results": []}]}
|
Ariffiq99/e_care_Ba1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"multiple-choice",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:32:56+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
mp1704/qwen_1.8b_sft_full_3
| null |
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:35:20+00:00
|
text-generation
|
transformers
|
{}
|
migueldeguzmandev/GPT2XL_RLLMv18-4
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null |
2024-04-24T04:36:46+00:00
|
|
null | null |
{}
|
shiml20/splat
| null |
[
"region:us"
] | null |
2024-04-24T04:37:23+00:00
|
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2_base_1.5
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3070
- Wer: 0.2230
- Cer: 0.0794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.4392 | 1.0 | 500 | 0.6651 | 0.4226 | 0.1684 |
| 0.9721 | 2.0 | 1000 | 0.6476 | 0.4063 | 0.1641 |
| 0.7793 | 3.0 | 1500 | 0.5237 | 0.3547 | 0.1412 |
| 0.6246 | 4.0 | 2000 | 0.4705 | 0.3275 | 0.1271 |
| 0.5062 | 5.0 | 2500 | 0.4313 | 0.3013 | 0.1147 |
| 0.4084 | 6.0 | 3000 | 0.3873 | 0.2798 | 0.1059 |
| 0.324 | 7.0 | 3500 | 0.3632 | 0.2534 | 0.0945 |
| 0.2531 | 8.0 | 4000 | 0.3387 | 0.2362 | 0.0857 |
| 0.197 | 9.0 | 4500 | 0.3219 | 0.2267 | 0.0806 |
| 0.1605 | 10.0 | 5000 | 0.3070 | 0.2230 | 0.0794 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2_base_1.5", "results": []}]}
|
Myriam123/wav2vec2_base_1.5
| null |
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:37:37+00:00
|
text-generation
|
transformers
|
# Meta-Llama-3-8B-Uninstruct-function-calling-json-mode-model_stock-v0.1
Meta-Llama-3-8B-Uninstruct-function-calling-json-mode-model_stock-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode](https://huggingface.co/hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode)
* [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B)
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode
parameters:
density: 1.0
weight: 0.7
layer_range: [0, 32]
- model: NousResearch/Meta-Llama-3-8B
layer_range: [0, 32]
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [0, 32]
merge_method: model_stock
base_model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Nhoodie/Meta-Llama-3-8B-Uninstruct-function-calling-json-mode-model_stock-v0.1"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
{"license": "other", "tags": ["merge", "mergekit", "lazymergekit", "hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode", "NousResearch/Meta-Llama-3-8B", "NousResearch/Meta-Llama-3-8B-Instruct"], "base_model": ["hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode", "NousResearch/Meta-Llama-3-8B", "NousResearch/Meta-Llama-3-8B-Instruct"], "license_name": "llama3", "license_link": "LICENSE"}
|
Nhoodie/Meta-Llama-3-8B-Uninstruct-function-calling-json-mode-model_stock-v0.1
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode",
"NousResearch/Meta-Llama-3-8B",
"NousResearch/Meta-Llama-3-8B-Instruct",
"conversational",
"base_model:hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:37:43+00:00
|
null | null |
{}
|
elinaparajuli/Codegemma-finetuned
| null |
[
"region:us"
] | null |
2024-04-24T04:38:37+00:00
|
|
text2text-generation
|
transformers
|
{}
|
lkid08/xpath_generation_model-5k-dataset
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:41:43+00:00
|
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_0cfe7cd601f511efa831d63e49e237a3_from_EleutherAI_pythia-14m
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_0cfe7cd601f511efa831d63e49e237a3_from_EleutherAI_pythia-14m", "results": []}]}
|
AlignmentResearch/robust_llm_0cfe7cd601f511efa831d63e49e237a3_from_EleutherAI_pythia-14m
| null |
[
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:42:30+00:00
|
null | null |
{}
|
PowerBombInc/RomanReigns
| null |
[
"region:us"
] | null |
2024-04-24T04:42:46+00:00
|
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/verifiers-for-code/Llama-3-LlamaPlanner
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-LlamaPlanner-GGUF/resolve/main/Llama-3-LlamaPlanner.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 -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["code"], "datasets": ["verifiers-for-code/CodeNet-16K", "verifiers-for-code/CodeNet-Planner"], "base_model": "verifiers-for-code/Llama-3-LlamaPlanner", "quantized_by": "mradermacher"}
|
mradermacher/Llama-3-LlamaPlanner-GGUF
| null |
[
"transformers",
"gguf",
"code",
"en",
"dataset:verifiers-for-code/CodeNet-16K",
"dataset:verifiers-for-code/CodeNet-Planner",
"base_model:verifiers-for-code/Llama-3-LlamaPlanner",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:43:14+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_2af87ec601f511ef91b4d63e49e237a3_from_EleutherAI_pythia-70m
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "robust_llm_2af87ec601f511ef91b4d63e49e237a3_from_EleutherAI_pythia-70m", "results": []}]}
|
AlignmentResearch/robust_llm_2af87ec601f511ef91b4d63e49e237a3_from_EleutherAI_pythia-70m
| null |
[
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:43:21+00:00
|
null | null |
{}
|
TrgTuan10/db_dpt_ReVi
| null |
[
"safetensors",
"region:us"
] | null |
2024-04-24T04:43:56+00:00
|
|
text-generation
|
transformers
|
# Llama-3-8B-Web-GGUf
- This is quantized version of [McGill-NLP/Llama-3-8B-Web](https://huggingface.co/McGill-NLP/Llama-3-8B-Web) created using llama.cpp
## Model Description
Our first agent is a finetuned [`Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model, which was recently released by Meta GenAI team. We have finetuned this model on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) dataset, which contains over 100K instances of web navigation and dialogue, each collected and verified by expert annotators. We use a 24K curated subset for training the data. The training and evaluation data is available on [Huggingface Hub as `McGill-NLP/WebLINX`](https://huggingface.co/datasets/McGill-NLP/WebLINX).
**It surpasses GPT-4V (zero-shot `*`) by over 18% on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark**, achieving an overall score of 28.8% on the out-of-domain test splits (compared to 10.5% for GPT-4V). It chooses more useful links (34.1% vs 18.9% *seg-F1*), clicks on more relevant elements (27.1% vs 13.6% *IoU*) and formulates more aligned responses (37.5% vs 3.1% *chr-F1*).
## About `WebLlama`
| `WebLlama` | The goal of our project is to build effective human-centric agents for browsing the web. We don't want to replace users, but equip them with powerful assistants. |
|:---: | :---|
| Modeling | We are build on top of cutting edge libraries for training Llama agents on web navigation tasks. We will provide training scripts, optimized configs, and instructions for training cutting-edge Llamas. |
| Evaluation | Benchmarks for testing Llama models on real-world web browsing. This include *human-centric* browsing through dialogue ([`WebLINX`](https://mcgill-nlp.github.io/weblinx/)), and we will soon add more benchmarks for automatic web navigation (e.g. Mind2Web). |
| Data | Our first model is finetuned on over 24K instances of web interactions, including `click`, `textinput`, `submit`, and dialogue acts. We want to continuously curate, compile and release datasets for training better agents. |
| Deployment | We want to make it easy to integrate Llama models with existing deployment platforms, including Playwright, Selenium, and BrowserGym. We are currently focusing on making this a reality. |
## Evaluation
We believe short demo videos showing how well an agent performs is NOT enough to judge an agent. Simply put, **we do not know if we have a good agent if we do not have good benchmarks.** We need to systematically evaluate agents on wide range of tasks, spanning from simple instruction-following web navigation to complex dialogue-guided browsing.
<img src="assets/WebLINXTestSplits.png" style="width: 100%; max-width:800px"/>
This is why we chose [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) as our first benchmark. In addition to the training split, the benchmark has 4 real-world splits, with the goal of testing multiple dimensions of generalization: new websites, new domains, unseen geographic locations, and scenarios where the *user cannot see the screen and relies on dialogue*. It also covers 150 websites, including booking, shopping, writing, knowledge lookup, and even complex tasks like manipulating spreadsheets.
## Data
Although the 24K training examples from [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) provide a good starting point for training a capable agent, we believe that more data is needed to train agents that can generalize to a wide range of web navigation tasks. Although it has been trained and evaluated on 150 websites, there are millions of websites that has never been seen by the model, with new ones being created every day.
**This motivates us to continuously curate, compile and release datasets for training better agents.** As an immediate next step, we will be incorporating `Mind2Web`'s training data into the equation, which also covers over 100 websites.
## Deployment
We are working hard to make it easy for you to deploy Llama web agents to the web. We want to integrate `WebLlama` with existing deployment platforms, including Microsoft's Playwright, ServiceNow Research's BrowserGym, and other partners.
## Code
The code for finetuning the model and evaluating it on the [`WebLINX`](https://mcgill-nlp.github.io/weblinx/) benchmark is available now. You can find the detailed instructions in [modeling](https://github.com/McGill-NLP/webllama/tree/main/modeling).
|
{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["agents", "agent", "llm", "llama"], "datasets": ["McGill-NLP/WebLINX"], "base_model": "McGill-NLP/Llama-3-8B-Web", "pipeline_tag": "text-generation"}
|
QuantFactory/Llama-3-8B-Web-GGUF
| null |
[
"transformers",
"gguf",
"agents",
"agent",
"llm",
"llama",
"text-generation",
"en",
"dataset:McGill-NLP/WebLINX",
"base_model:McGill-NLP/Llama-3-8B-Web",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:45:28+00:00
|
null |
espnet
|
{"language": "ko", "license": "mit", "tags": ["espnet"], "datasets": ["ksponspeech", "aihub/463"], "id": "pkyoung/ma16k2401b", "base_model": "ESPnetASRModel"}
|
pkyoung/ma16k2401b
| null |
[
"espnet",
"ko",
"dataset:ksponspeech",
"dataset:aihub/463",
"base_model:ESPnetASRModel",
"license:mit",
"region:us"
] | null |
2024-04-24T04:45:45+00:00
|
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/CroissantCrusader/FrenchBaguette
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/FrenchBaguette-GGUF/resolve/main/FrenchBaguette.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 -->
|
{"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "CroissantCrusader/FrenchBaguette", "quantized_by": "mradermacher"}
|
mradermacher/FrenchBaguette-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:CroissantCrusader/FrenchBaguette",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:45:55+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/birgermoell/NorskGPT-ChimeraLlama-3
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/NorskGPT-ChimeraLlama-3-GGUF/resolve/main/NorskGPT-ChimeraLlama-3.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 -->
|
{"language": ["en"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "birgermoell/NorskGPT-ChimeraLlama-3", "quantized_by": "mradermacher"}
|
mradermacher/NorskGPT-ChimeraLlama-3-GGUF
| null |
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:birgermoell/NorskGPT-ChimeraLlama-3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:45:55+00:00
|
null | null |
# Function Calling and Tool Use LLaMA Models
This repository contains two main versions of LLaMA models fine-tuned for function calling and tool use capabilities:
1. Fine-tuned version of the `LLama3-8b-instruct` model
2. `tinyllama` - a smaller model version
For each version, the following variants are available:
- 16-bit quantized model
- 4-bit quantized model
- GGFU format for use with llama.cpp
## Dataset
The models were fine-tuned using a modified version of the `ilacai/glaive-function-calling-v2-sharegpt` dataset, which can be found at [unclecode/glaive-function-calling-llama3](https://huggingface.co/datasets/unclecode/glaive-function-calling-llama3).
## Usage
To learn how to use these models, refer to the Colab notebook: [](https://tinyurl.com/ucfllm)
This is the first version of the models, and work is in progress to further train them with multi-tool detection and native tool binding support.
## Library and Tools Support
A library is being developed to manage tools and add tool support for major LLMs, regardless of their built-in capabilities. You can find examples and contribute to the library at the following repository:
[https://github.com/unclecode/fllm](https://github.com/unclecode/fllm)
Please open an issue in the repository for any bugs or collaboration requests.
## Other Models
Here are links to other related models:
- [unclecode/llama3-function-call-lora-adapter-240424](https://huggingface.co/unclecode/llama3-function-call-lora-adapter-240424)
- [unclecode/llama3-function-call-16bit-240424](https://huggingface.co/unclecode/llama3-function-call-16bit-240424)
- [unclecode/llama3-function-call-4bit-240424](https://huggingface.co/unclecode/llama3-function-call-4bit-240424)
- [unclecode/llama3-function-call-Q4_K_M_GGFU-240424](https://huggingface.co/unclecode/llama3-function-call-Q4_K_M_GGFU-240424)
- [unclecode/tinyllama-function-call-lora-adapter-250424](https://huggingface.co/unclecode/tinyllama-function-call-lora-adapter-250424)
- [unclecode/tinyllama-function-call-16bit-250424](https://huggingface.co/unclecode/tinyllama-function-call-16bit-250424)
- [unclecode/tinyllama-function-call-Q4_K_M_GGFU-250424](https://huggingface.co/unclecode/tinyllama-function-call-Q4_K_M_GGFU-250424)
## License
These models are released under the Apache 2.0 license.
|
{"license": "apache-2.0", "tags": ["function calling", "tool use", "llama", "llama3", "tinyllama", "instruct-tuned", "4-bit quantization", "ggfu"]}
|
unclecode/llama3-function-call-lora-adapter-240424
| null |
[
"safetensors",
"function calling",
"tool use",
"llama",
"llama3",
"tinyllama",
"instruct-tuned",
"4-bit quantization",
"ggfu",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T04:46:19+00:00
|
text-generation
|
transformers
|
# ✨ Falcon-7B-Instruct
**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
* **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B-Instruct.
# Model Card for Falcon-7B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
| **Data source** | **Fraction** | **Tokens** | **Description** |
|--------------------|--------------|------------|-----------------------------------|
| [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat |
| [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct |
| [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct |
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
Note that this model variant is not optimized for NLP benchmarks.
## Technical Specifications
For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B-Instruct is made available under the Apache 2.0 license.
## Contact
[email protected]
|
{"language": ["en"], "license": "apache-2.0", "datasets": ["tiiuae/falcon-refinedweb"], "inference": true, "widget": [{"text": "Hey Falcon! Any recommendations for my holidays in Abu Dhabi?", "example_title": "Abu Dhabi Trip"}, {"text": "What's the Everett interpretation of quantum mechanics?", "example_title": "Q/A: Quantum & Answers"}, {"text": "Give me a list of the top 10 dive sites you would recommend around the world.", "example_title": "Diving Top 10"}, {"text": "Can you tell me more about deep-water soloing?", "example_title": "Extreme sports"}, {"text": "Can you write a short tweet about the Apache 2.0 release of our latest AI model, Falcon LLM?", "example_title": "Twitter Helper"}, {"text": "What are the responsabilities of a Chief Llama Officer?", "example_title": "Trendy Jobs"}]}
|
Poorvaja/Model
| null |
[
"transformers",
"pytorch",
"falcon",
"text-generation",
"custom_code",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:46:22+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
santoshsto/mistral-7b-java-FINETUNED-4bit
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T04:48:26+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Eurdem/Bombus_3x8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Bombus_3x8B-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/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q2_K.gguf) | Q2_K | 7.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.IQ3_XS.gguf) | IQ3_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q3_K_S.gguf) | Q3_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.IQ3_S.gguf) | IQ3_S | 8.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.IQ3_M.gguf) | IQ3_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q3_K_M.gguf) | Q3_K_M | 9.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q3_K_L.gguf) | Q3_K_L | 10.2 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.IQ4_XS.gguf) | IQ4_XS | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q4_K_S.gguf) | Q4_K_S | 11.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q4_K_M.gguf) | Q4_K_M | 11.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q5_K_S.gguf) | Q5_K_S | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q5_K_M.gguf) | Q5_K_M | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q6_K.gguf) | Q6_K | 15.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Bombus_3x8B-GGUF/resolve/main/Bombus_3x8B.Q8_0.gguf) | Q8_0 | 20.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["moe", "merge", "llama-3"], "base_model": "Eurdem/Bombus_3x8B", "quantized_by": "mradermacher"}
|
mradermacher/Bombus_3x8B-GGUF
| null |
[
"transformers",
"gguf",
"moe",
"merge",
"llama-3",
"en",
"base_model:Eurdem/Bombus_3x8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:48:29+00:00
|
text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Meta-Llama-3-8B-Instruct_fictional_Chinese_v3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 72
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "other", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct_fictional_Chinese_v3", "results": []}]}
|
yzhuang/Meta-Llama-3-8B-Instruct_fictional_Chinese_v3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:49:51+00:00
|
null | null |
{}
|
JuniorThap/my_awesome_opus_books_model
| null |
[
"region:us"
] | null |
2024-04-24T04:50:57+00:00
|
|
text-to-image
|
diffusers
|
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "realcartoon-special-sp1"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/realcartoon-special-sp1)
Model link: [View model](https://modelslab.com/models/realcartoon-special-sp1)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "realcartoon-special-sp1",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}
|
stablediffusionapi/realcartoon-special-sp1
| null |
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-24T04:50:57+00:00
|
null | null |
{}
|
vangard703/DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-1-iteration-smi-2-candidiate-6e1-confidence
| null |
[
"region:us"
] | null |
2024-04-24T04:51:05+00:00
|
|
null | null |
{}
|
Phuree/image_captioning
| null |
[
"region:us"
] | null |
2024-04-24T04:51:21+00:00
|
|
text-generation
|
transformers
|
{"license": "mit"}
|
Dudep/phi2-dpo-M2-Gemini
| null |
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:51:25+00:00
|
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}
|
mp27/Enlighten_Instruct
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null |
2024-04-24T04:51:26+00:00
|
reinforcement-learning
|
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://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hossniper/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
{"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]}
|
hossniper/ppo-Huggy
| null |
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null |
2024-04-24T04:52:01+00:00
|
multiple-choice
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-mathqa
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5056
- Accuracy: 0.3445
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5584 | 1.0 | 2970 | 1.5429 | 0.3029 |
| 1.485 | 2.0 | 5940 | 1.4965 | 0.3328 |
| 1.3677 | 3.0 | 8910 | 1.5056 | 0.3445 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-mathqa", "results": []}]}
|
nickrwu/distilbert-base-uncased-finetuned-mathqa
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"multiple-choice",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:53:34+00:00
|
text-generation
|
transformers
|
# opus-samantha-phi-3-mini-4k
opus-samantha-phi-3-mini-4k is an SFT fine-tuned version of microsoft/Phi-3-mini-4k-instruct using a custom training dataset.
This model was made with [Phinetune](https://colab.research.google.com/drive/1e8LILflDQ2Me52hwS7uIfuJ9DxE2oQzM#scrollTo=LxOzYC4oabaN)
## Process
- Learning Rate: 2e-05
- Maximum Sequence Length: 2048
- Dataset: macadeliccc/opus_samantha
- Split: train
## 💻 Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"macadeliccc/opus-samantha-phi-3-mini-4k",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
messages = [
{"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
|
{"license": "apache-2.0"}
|
macadeliccc/opus-samantha-phi-3-mini-4k
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:54:16+00:00
|
text-generation
|
transformers
|
{}
|
GuardisAI/Video-LLaVA-7B-GPTQ-4bit-V1
| null |
[
"transformers",
"safetensors",
"llava",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null |
2024-04-24T04:54:34+00:00
|
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flant-t5-small-function-calling-v2
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: 0.0000
- Rouge1: 57.6757
- Rouge2: 50.7633
- Rougel: 57.677
- Rougelsum: 57.677
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:------:|:---------:|:-------:|
| 0.0004 | 1.0 | 6250 | 0.0000 | 57.6757 | 50.7633 | 57.677 | 57.677 | 19.0 |
| 0.0002 | 2.0 | 12500 | 0.0000 | 57.6757 | 50.7633 | 57.677 | 57.677 | 19.0 |
| 0.0001 | 3.0 | 18750 | 0.0000 | 57.6757 | 50.7633 | 57.677 | 57.677 | 19.0 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/flan-t5-small", "model-index": [{"name": "flant-t5-small-function-calling-v2", "results": []}]}
|
jrcastropy/flan-t5-small-query-extraction-v2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:56:02+00:00
|
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
AlanYR/mpr_tuned_kakao
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:56:14+00:00
|
feature-extraction
|
transformers
|
{"license": "apache-2.0"}
|
momoyukki/myktesetmodel
| null |
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:56:14+00:00
|
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MTT_Cipher
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "MTT_Cipher", "results": []}]}
|
xnnng/MTT_Cipher
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T04:56:18+00:00
|
text-generation
|
transformers
|
# Nxcode-CQ-7B-orpo
## Introduction
Nxcode-CQ-7B-orpo is an ORPO fine-tune of Qwen/CodeQwen1.5-7B-Chat on 100k samples ours datasets.
* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.
## [Evalplus](https://github.com/evalplus/evalplus)
| EvalPlus | pass@1 |
| --- | --- |
| HumanEval | 86.0 |
| HumanEval+ | 81.1 |
| MBPP(v0.2.0) | 82.5 |
| MBPP+(v0.2.0) | 70.4 |
We use a simple template to generate the solution for evalplus:
```python
"Complete the following Python function:\n{prompt}"
```
[Evalplus Leaderboard](https://evalplus.github.io/leaderboard.html)
| Models | HumanEval | HumanEval+|
|------ | ------ | ------ |
| GPT-4-Turbo (April 2024)| 90.2| 86.6|
| GPT-4 (May 2023)| 88.4| 81.17|
| GPT-4-Turbo (Nov 2023)| 85.4| 79.3|
| CodeQwen1.5-7B-Chat| 83.5| 78.7|
| claude-3-opus (Mar 2024)| 82.9| 76.8|
| DeepSeek-Coder-33B-instruct| 81.1| 75.0|
| WizardCoder-33B-V1.1| 79.9| 73.2|
| OpenCodeInterpreter-DS-33B| 79.3| 73.8|
| speechless-codellama-34B-v2.0| 77.4| 72|
| GPT-3.5-Turbo (Nov 2023)| 76.8| 70.7|
| Llama3-70B-instruct| 76.2| 70.7|
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. You should use transformer version 4.39 if you receive an error when loading the tokenizer
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"NTQAI/Nxcode-CQ-7B-orpo",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("NTQAI/Nxcode-CQ-7B-orpo")
prompt = """Complete the following Python function:
from typing import List
def has_close_elements(numbers: List[float], threshold: float) -> bool:
""" Check if in given list of numbers, are any two numbers closer to each other than
given threshold.
>>> has_close_elements([1.0, 2.0, 3.0], 0.5)
False
>>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)
True
"""
"""
messages = [
{"role": "user", "content": prompt}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
res = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
```
### Contact information
For personal communication related to this project, please contact Nha Nguyen Van ([email protected]).
|
{"license": "mit", "tags": ["code"], "pipeline_tag": "text-generation"}
|
NTQAI/Nxcode-CQ-7B-orpo
| null |
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:56:38+00:00
|
text-generation
|
transformers
|
{"license": "mit"}
|
migueldeguzmandev/GPT2XL_RLLMv17-1
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T04:57:10+00:00
|
|
null | null |
{}
|
samayl24/convnext-tiny-224-driverbox-diverse
| null |
[
"region:us"
] | null |
2024-04-24T04:57:59+00:00
|
|
text2text-generation
|
transformers
|
{}
|
anhmanucian1903/T5-small-finetuned-vi
| null |
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:00:14+00:00
|
|
null | null |
{"license": "bigscience-bloom-rail-1.0"}
|
ARunKuMaR22/arun
| null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null |
2024-04-24T05:00:54+00:00
|
|
text-generation
| null |
# Eurus-7b-sft-GGUF
- This is quantized version of [openbmb/Eurus-7b-sft](https://huggingface.co/openbmb/Eurus-7b-sft) created using llama.cpp
# Model Description
Eurus-7B-SFT is fine-tuned from Mistral-7B on all correct actions in UltraInteract, mixing a small proportion of UltraChat, ShareGPT, and OpenOrca examples.
It achieves better performance than other open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases.
## Usage
We apply tailored prompts for coding and math, consistent with UltraInteract data formats:
**Coding**
```
[INST] Write Python code to solve the task:
{Instruction} [/INST]
```
**Math-CoT**
```
[INST] Solve the following math problem step-by-step.
Simplify your answer as much as possible. Present your final answer as \\boxed{Your Answer}.
{Instruction} [/INST]
```
**Math-PoT**
```
[INST] Tool available:
[1] Python interpreter
When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.
Solve the following math problem step-by-step.
Simplify your answer as much as possible.
{Instruction} [/INST]
```
## Evaluation
- Eurus, both the 7B and 70B variants, achieve the best overall performance among open-source models of similar sizes. Eurus even outperforms specialized models in corresponding domains in many cases. Notably, Eurus-7B outperforms baselines that are 5× larger, and Eurus-70B achieves better performance than GPT-3.5 Turbo.
- Preference learning with UltraInteract can further improve performance, especially in math and the multi-turn ability.
<img src="figures_main_exp.png" alt="stats" style="zoom: 40%;" />
|
{"license": "apache-2.0", "tags": ["reasoning"], "datasets": ["openbmb/UltraInteract_sft", "stingning/ultrachat", "openchat/openchat_sharegpt4_dataset", "Open-Orca/OpenOrca"], "pipeline_tag": "text-generation"}
|
QuantFactory/Eurus-7b-sft-GGUF
| null |
[
"gguf",
"reasoning",
"text-generation",
"dataset:openbmb/UltraInteract_sft",
"dataset:stingning/ultrachat",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:Open-Orca/OpenOrca",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:02:18+00:00
|
token-classification
|
sklearn
|
{"language": ["ar"], "library_name": "sklearn", "tags": ["Text-segmentation"], "pipeline_tag": "token-classification"}
|
Alshargi/arabic-msa-dialects-segmentation
| null |
[
"sklearn",
"Text-segmentation",
"token-classification",
"ar",
"has_space",
"region:us"
] | null |
2024-04-24T05:04:01+00:00
|
|
text-classification
|
transformers
|
{}
|
yamaguchi-kota/distilbert-base-uncased-finetuned-emotion
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:04:35+00:00
|
|
null | null |
{}
|
ShenaoZ/0.01_ablation_4iters_bs256_nodpo_iter_4
| null |
[
"region:us"
] | null |
2024-04-24T05:04:56+00:00
|
|
null | null |
{}
|
pbpv6b/my_awesome_model
| null |
[
"region:us"
] | null |
2024-04-24T05:05:41+00:00
|
|
text-generation
|
transformers
|
# RedPajama-INCITE-7B-Instruct
RedPajama-INCITE-7B-Instruct was developed by Together and leaders from the open-source AI community including Ontocord.ai, ETH DS3Lab, AAI CERC, Université de Montréal, MILA - Québec AI Institute, Stanford Center for Research on Foundation Models (CRFM), Stanford Hazy Research research group and LAION.
The model was fine-tuned for few-shot applications on the data of [GPT-JT](https://huggingface.co/togethercomputer/GPT-JT-6B-v1), with exclusion of tasks that overlap with the HELM core scenarios.
- Base Model: [RedPajama-INCITE-7B-Base](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base)
- Instruction-tuned Version: [RedPajama-INCITE-7B-Instruct](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Instruct)
- Chat Version: [RedPajama-INCITE-7B-Chat](https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Chat)
## Model Details
- **Developed by**: Together Computer.
- **Model type**: Language Model
- **Language(s)**: English
- **License**: Apache 2.0
- **Model Description**: A 6.9B parameter pretrained language model.
# Quick Start
Please note that the model requires `transformers` version >= 4.25.1.
## GPU Inference
This requires a GPU with 16GB memory.
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", torch_dtype=torch.float16)
model = model.to('cuda:0')
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
## GPU Inference in Int8
This requires a GPU with 12GB memory.
To run inference with int8, please ensure you have installed accelerate and bitandbytes. You can install them with the following command:
```bash
pip install accelerate
pip install bitsandbytes
```
Then you can run inference with int8 as follows:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
## CPU Inference
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
MIN_TRANSFORMERS_VERSION = '4.25.1'
# check transformers version
assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
# init
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Instruct", torch_dtype=torch.bfloat16)
# infer
prompt = "Q: The capital of France is?\nA:"
inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
input_length = inputs.input_ids.shape[1]
outputs = model.generate(
**inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
)
token = outputs.sequences[0, input_length:]
output_str = tokenizer.decode(token)
print(output_str)
"""
Paris
"""
```
Please note that since `LayerNormKernelImpl` is not implemented in fp16 for CPU, we use `bfloat16` for CPU inference.
# Uses
## Direct Use
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
It is the responsibility of the end user to ensure that the model is used in a responsible and ethical manner.
#### Out-of-Scope Use
RedPajama-INCITE-7B-Instruct is a language model and may not perform well for other use cases outside of its intended scope.
For example, it may not be suitable for use in safety-critical applications or for making decisions that have a significant impact on individuals or society.
It is important to consider the limitations of the model and to only use it for its intended purpose.
#### Misuse and Malicious Use
RedPajama-INCITE-7B-Instruct is designed for language modeling.
Misuse of the model, such as using it to engage in illegal or unethical activities, is strictly prohibited and goes against the principles of the project.
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating fake news, misinformation, or propaganda
- Promoting hate speech, discrimination, or violence against individuals or groups
- Impersonating individuals or organizations without their consent
- Engaging in cyberbullying or harassment
- Defamatory content
- Spamming or scamming
- Sharing confidential or sensitive information without proper authorization
- Violating the terms of use of the model or the data used to train it
- Creating automated bots for malicious purposes such as spreading malware, phishing scams, or spamming
## Limitations
RedPajama-INCITE-7B-Instruct, like other language models, has limitations that should be taken into consideration.
For example, the model may not always provide accurate or relevant answers, particularly for questions that are complex, ambiguous, or outside of its training data.
We therefore welcome contributions from individuals and organizations, and encourage collaboration towards creating a more robust and inclusive chatbot.
## Training
**Training Data**
Please refer to [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T)
**Training Procedure**
- **Hardware:** 8 A100
- **Optimizer:** Adam
- **Gradient Accumulations**: 1
- **Num of Tokens:** 1B tokens
- **Learning rate:** 1e-5
## Community
Join us on [Together Discord](https://discord.gg/6ZVDU8tTD4)
|
{"language": ["en"], "license": "apache-2.0", "datasets": ["togethercomputer/RedPajama-Data-1T", "togethercomputer/RedPajama-Data-Instruct"], "widget": [{"text": "Label the tweets as either 'positive', 'negative', 'mixed', or 'neutral': \n\nTweet: I can say that there isn't anything I would change.\nLabel: positive\n\nTweet: I'm not sure about this.\nLabel: neutral\n\nTweet: I liked some parts but I didn't like other parts.\nLabel: mixed\n\nTweet: I think the background image could have been better.\nLabel: negative\n\nTweet: I really like it.\nLabel:", "example_title": "Sentiment Analysis"}, {"text": "Please answer the following question:\n\nQuestion: What is the capital of Canada?\nAnswer: Ottawa\n\nQuestion: What is the currency of Switzerland?\nAnswer: Swiss franc\n\nQuestion: In which country is Wisconsin located?\nAnswer:", "example_title": "Question Answering"}, {"text": "Given a news article, classify its topic.\nPossible labels: 1. World 2. Sports 3. Business 4. Sci/Tech\n\nArticle: A nearby star thought to harbor comets and asteroids now appears to be home to planets, too.\nLabel: Sci/Tech\n\nArticle: Soaring crude prices plus worries about the economy and the outlook for earnings are expected to hang over the stock market next week during the depth of the summer doldrums.\nLabel: Business\n\nArticle: Murtagh a stickler for success Northeastern field hockey coach Cheryl Murtagh doesn't want the glare of the spotlight that shines on her to detract from a team that has been the America East champion for the past three years and has been to the NCAA tournament 13 times.\nLabel::", "example_title": "Topic Classification"}, {"text": "Paraphrase the given sentence into a different sentence.\n\nInput: Can you recommend some upscale restaurants in New York?\nOutput: What upscale restaurants do you recommend in New York?\n\nInput: What are the famous places we should not miss in Paris?\nOutput: Recommend some of the best places to visit in Paris?\n\nInput: Could you recommend some hotels that have cheap price in Zurich?\nOutput:", "example_title": "Paraphrasing"}, {"text": "Given a review from Amazon's food products, the task is to generate a short summary of the given review in the input.\n\nInput: I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most.\nOutput: Good Quality Dog Food\n\nInput: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as 'Jumbo'.\nOutput: Not as Advertised\n\nInput: My toddler loves this game to a point where he asks for it. That's a big thing for me. Secondly, no glitching unlike one of their competitors (PlayShifu). Any tech I don\u2019t have to reach out to support for help is a good tech for me. I even enjoy some of the games and activities in this. Overall, this is a product that shows that the developers took their time and made sure people would not be asking for refund. I\u2019ve become bias regarding this product and honestly I look forward to buying more of this company\u2019s stuff. Please keep up the great work.\nOutput:", "example_title": "Text Summarization"}, {"text": "Identify which sense of a word is meant in a given context.\n\nContext: The river overflowed the bank.\nWord: bank\nSense: river bank\n\nContext: A mouse takes much more room than a trackball.\nWord: mouse\nSense: computer mouse\n\nContext: The bank will not be accepting cash on Saturdays.\nWord: bank\nSense: commercial (finance) banks\n\nContext: Bill killed the project\nWord: kill\nSense:", "example_title": "Word Sense Disambiguation"}, {"text": "Given a pair of sentences, choose whether the two sentences agree (entailment)/disagree (contradiction) with each other.\nPossible labels: 1. entailment 2. contradiction\n\nSentence 1: The skier was on the edge of the ramp. Sentence 2: The skier was dressed in winter clothes.\nLabel: entailment\n\nSentence 1: The boy skated down the staircase railing. Sentence 2: The boy is a newbie skater.\nLabel: contradiction\n\nSentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A couple riding in a golf cart.\nLabel:", "example_title": "Natural Language Inference"}], "inference": {"parameters": {"temperature": 0.7, "top_p": 0.7, "top_k": 50, "max_new_tokens": 128}}}
|
titanbot/ct2-int8-redpajama-7b-instruct
| null |
[
"transformers",
"gpt_neox",
"text-generation",
"en",
"dataset:togethercomputer/RedPajama-Data-1T",
"dataset:togethercomputer/RedPajama-Data-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:06:29+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-14m_mz-130_PasswordMatch_n-its-10-seed-0
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-130_PasswordMatch_n-its-10-seed-0", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-14m_mz-130_PasswordMatch_n-its-10-seed-0
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:08:00+00:00
|
fill-mask
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Pretraining_MFM_v2
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/deberta-base", "model-index": [{"name": "Pretraining_MFM_v2", "results": []}]}
|
JJ-Tae/Pretraining_MFM_v2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"deberta",
"fill-mask",
"generated_from_trainer",
"base_model:microsoft/deberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:08:25+00:00
|
null | null |
{}
|
4ndr3lu15/glove300
| null |
[
"region:us"
] | null |
2024-04-24T05:09:07+00:00
|
|
null | null |
{}
|
angelosarjeant01/AngeloSarjeant
| null |
[
"region:us"
] | null |
2024-04-24T05:09:44+00:00
|
|
text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-2-iteration-change-ref
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5849
- Rewards/chosen: -2.3410
- Rewards/rejected: -2.9843
- Rewards/accuracies: 0.6706
- Rewards/margins: 0.6433
- Rewards/mix Margin: 0.2179
- Logps/rejected: -580.8327
- Logps/chosen: -486.8375
- Logits/rejected: -1.4430
- Logits/chosen: -1.5066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.17.1
- Tokenizers 0.15.1
|
{"tags": ["trl", "dpo", "generated_from_trainer"], "model-index": [{"name": "DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-2-iteration-change-ref", "results": []}]}
|
vangard703/DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-2-iteration-change-ref
| null |
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:09:55+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
santoshsto/mistral-7b-javascript-FINETUNED-4bit
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T05:11:47+00:00
|
null | null |
{"license": "apache-2.0"}
|
psundareswar/rare-hack-llama
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:12:17+00:00
|
|
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
AlanYR/mpr_tuned_kobert
| null |
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:13:13+00:00
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_sum_bart_base_model
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7789
- Rouge1: 0.4137
- Rouge2: 0.3037
- Rougel: 0.3749
- Rougelsum: 0.3747
- Gen Len: 19.9959
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9855 | 1.0 | 1764 | 0.8474 | 0.4122 | 0.303 | 0.3726 | 0.3726 | 19.9908 |
| 0.8409 | 2.0 | 3528 | 0.7938 | 0.4138 | 0.3044 | 0.3752 | 0.3751 | 19.9946 |
| 0.7872 | 3.0 | 5292 | 0.7776 | 0.4174 | 0.308 | 0.3783 | 0.3782 | 19.9928 |
| 0.7485 | 4.0 | 7056 | 0.7789 | 0.4137 | 0.3037 | 0.3749 | 0.3747 | 19.9959 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "test_sum_bart_base_model", "results": []}]}
|
InfinityC/test_sum_bart_base_model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:14:01+00:00
|
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
AlanYR/mpr_tuned_bert
| null |
[
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:14:50+00:00
|
text-generation
| null |
# openbmb/Eurus-7b-kto-GGUF
- This is quantized version of [openbmb/Eurus-7b-kto](https://huggingface.co/openbmb/Eurus-7b-kto)
# Model Description
Eurus-7B-KTO is [KTO](https://arxiv.org/abs/2402.01306) fine-tuned from [Eurus-7B-SFT](https://huggingface.co/openbmb/Eurus-7b-sft) on all multi-turn trajectory pairs in [UltraInteract](https://huggingface.co/openbmb/UltraInteract) and all pairs in [UltraFeedback](https://huggingface.co/openbmb/UltraFeedback).
It achieves the best overall performance among open-source models of similar sizes and even outperforms specialized models in corresponding domains in many cases. Notably, Eurus-7B-KTO outperforms baselines that are 5× larger.
## Usage
We apply tailored prompts for coding and math, consistent with UltraInteract data formats:
**Coding**
```
[INST] Write Python code to solve the task:
{Instruction} [/INST]
```
**Math-CoT**
```
[INST] Solve the following math problem step-by-step.
Simplify your answer as much as possible. Present your final answer as \\boxed{Your Answer}.
{Instruction} [/INST]
```
**Math-PoT**
```
[INST] Tool available:
[1] Python interpreter
When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment.
Solve the following math problem step-by-step.
Simplify your answer as much as possible.
{Instruction} [/INST]
```
## Evaluation
- Eurus, both the 7B and 70B variants, achieve the best overall performance among open-source models of similar sizes. Eurus even outperforms specialized models in corresponding domains in many cases. Notably, Eurus-7B outperforms baselines that are 5× larger, and Eurus-70B achieves better performance than GPT-3.5 Turbo.
- Preference learning with UltraInteract can further improve performance, especially in math and the multi-turn ability.
<img src="figures_main_exp.png" alt="stats" style="zoom: 40%;" />
|
{"license": "apache-2.0", "tags": ["reasoning", "preference_learning", "kto"], "datasets": ["openbmb/UltraFeedback", "openbmb/UltraInteract_pair"], "pipeline_tag": "text-generation", "base_model": "openbmb/Eurus-7b-kto"}
|
QuantFactory/Eurus-7b-kto-GGUF
| null |
[
"gguf",
"reasoning",
"preference_learning",
"kto",
"text-generation",
"dataset:openbmb/UltraFeedback",
"dataset:openbmb/UltraInteract_pair",
"arxiv:2402.01306",
"base_model:openbmb/Eurus-7b-kto",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:15:49+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/llama-3-dragonmaid-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-dragonmaid-8B-GGUF/resolve/main/llama-3-dragonmaid-8B.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 -->
|
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["nsfw", "not-for-all-audiences", "experimental"], "datasets": ["ResplendentAI/NSFW_RP_Format_NoQuote"], "base_model": "nbeerbower/llama-3-dragonmaid-8B", "license_name": "llama3", "quantized_by": "mradermacher"}
|
mradermacher/llama-3-dragonmaid-8B-GGUF
| null |
[
"transformers",
"gguf",
"nsfw",
"not-for-all-audiences",
"experimental",
"en",
"dataset:ResplendentAI/NSFW_RP_Format_NoQuote",
"base_model:nbeerbower/llama-3-dragonmaid-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:17:10+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/timpal0l/dolphin-2.9-llama3-8b-flashback
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF/resolve/main/dolphin-2.9-llama3-8b-flashback.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 -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["merge"], "base_model": "timpal0l/dolphin-2.9-llama3-8b-flashback", "quantized_by": "mradermacher"}
|
mradermacher/dolphin-2.9-llama3-8b-flashback-GGUF
| null |
[
"transformers",
"gguf",
"merge",
"en",
"base_model:timpal0l/dolphin-2.9-llama3-8b-flashback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:17:11+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-s_ade20k
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:17:39+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
arushisharma/gemma7b-finetuned-medical-summarization
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:17:45+00:00
|
text-generation
|
transformers
|
Quantizations of https://huggingface.co/dreamgen/WizardLM-2-7B
# From original readme
## Usage
❗<b>Note for model system prompts usage:</b>
<b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful,
detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
USER: Who are you? ASSISTANT: I am WizardLM.</s>......
```
<b> Inference WizardLM-2 Demo Script</b>
We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
|
{"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "wizardlm"], "inference": false, "pipeline_tag": "text-generation"}
|
duyntnet/WizardLM-2-7B-imatrix-GGUF
| null |
[
"transformers",
"gguf",
"imatrix",
"wizardlm",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-24T05:18:03+00:00
|
null | null |
# Llama-3-Chinese-8B-LoRA
<p align="center">
<a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca-3"><img src="https://ymcui.com/images/chinese-llama-alpaca-3-banner.png" width="600"/></a>
</p>
This repository contains **Llama-3-Chinese-8B-LoRA**, which is further pre-trained on [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) with 120 GB Chinese text corpora.
**Note: You must combine LoRA with the original [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) to obtain full weight.**
Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
## Others
- For full model, please see: https://huggingface.co/hfl/llama-3-chinese-8b
- For GGUF model (llama.cpp compatible), please see: https://huggingface.co/hfl/llama-3-chinese-8b-gguf
- If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
|
{"language": ["zh", "en"], "license": "apache-2.0", "base_model": "meta-llama/Meta-Llama-3-8B"}
|
hfl/llama-3-chinese-8b-lora
| null |
[
"safetensors",
"zh",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:18:05+00:00
|
automatic-speech-recognition
|
transformers
|
{}
|
Subhadeep/Bengali_Tine_Finetuned_tiny
| null |
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:18:07+00:00
|
|
text-to-image
|
diffusers
|
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "aniverse-v3-pruned"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/aniverse-v3-pruned)
Model link: [View model](https://modelslab.com/models/aniverse-v3-pruned)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "aniverse-v3-pruned",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN**
|
{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}
|
stablediffusionapi/aniverse-v3-pruned
| null |
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-24T05:18:13+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-ti_ade20k
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:18:14+00:00
|
|
null | null |
# Llama-3-Chinese-8B-Instruct-LoRA
<p align="center">
<a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca-3"><img src="https://ymcui.com/images/chinese-llama-alpaca-3-banner.png" width="600"/></a>
</p>
This repository contains **Llama-3-Chinese-8B-Instruct-LoRA**, which is further tuned with 5M instruction data on [Llama-3-Chinese-8B](https://huggingface.co/hfl/llama-3-chinese-8b).
**Note: You must combine LoRA with the original [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) to obtain full weight.**
Further details (performance, usage, etc.) should refer to GitHub project page: https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
## Others
- For full model, please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct
- For GGUF model (llama.cpp compatible), please see: https://huggingface.co/hfl/llama-3-chinese-8b-instruct-gguf
- If you have questions/issues regarding this model, please submit an issue through https://github.com/ymcui/Chinese-LLaMA-Alpaca-3
|
{"language": ["zh", "en"], "license": "apache-2.0", "base_model": "hfl/llama-3-chinese-8b"}
|
hfl/llama-3-chinese-8b-instruct-lora
| null |
[
"safetensors",
"zh",
"en",
"base_model:hfl/llama-3-chinese-8b",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T05:18:43+00:00
|
null | null |
{}
|
durga10/qwen2-llm
| null |
[
"region:us"
] | null |
2024-04-24T05:21:56+00:00
|
|
text-generation
|
transformers
|
{"license": "mit", "tags": ["code", "llama-factory"]}
|
sanyuan0704/hhh-8b
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"llama-factory",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:22:03+00:00
|
|
text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.001_ablation_5iters_bs256_nodpo_iter_3
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_2](https://huggingface.co/ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_2) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_2", "model-index": [{"name": "0.001_ablation_5iters_bs256_nodpo_iter_3", "results": []}]}
|
ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_5iters_bs256_nodpo_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:22:24+00:00
|
null | null |
{}
|
durga10/qwen1.5-llm
| null |
[
"gguf",
"region:us"
] | null |
2024-04-24T05:22:48+00:00
|
|
null | null |
{}
|
PowerBombInc/RomanReigns2
| null |
[
"region:us"
] | null |
2024-04-24T05:23:01+00:00
|
|
text-generation
|
transformers
|
# Model Card for free-solar-evo-v0.11
## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) AI Technical Team
## Method
- We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)
## Base Model
- free-solar-evo-model
|
{"language": ["ko", "en"], "license": "mit"}
|
freewheelin/free-solar-evo-v0.11
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:23:52+00:00
|
text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.01_ablation_5iters_bs256_nodpo_iter_3
This model is a fine-tuned version of [ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_2](https://huggingface.co/ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_2) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_2", "model-index": [{"name": "0.01_ablation_5iters_bs256_nodpo_iter_3", "results": []}]}
|
ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.01_ablation_5iters_bs256_nodpo_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:25:40+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-1", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:25:58+00:00
|
null | null |
{"license": "mit"}
|
pkyoung/ma16k2401c
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-24T05:26:01+00:00
|
|
null | null |
{}
|
aniketarahane/llama2_poker
| null |
[
"safetensors",
"region:us"
] | null |
2024-04-24T05:26:15+00:00
|
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-2
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-70m", "model-index": [{"name": "robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-2", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-70m_mz-130_PasswordMatch_n-its-10-seed-2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:27:07+00:00
|
null | null |
{}
|
janedsa/bert
| null |
[
"region:us"
] | null |
2024-04-24T05:27:25+00:00
|
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jeiku/Average_Normie_l3_v1_8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Average_Normie_l3_v1_8B-GGUF/resolve/main/Average_Normie_l3_v1_8B.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 -->
|
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "datasets": ["grimulkan/theory-of-mind"], "base_model": "jeiku/Average_Normie_l3_v1_8B", "quantized_by": "mradermacher"}
|
mradermacher/Average_Normie_l3_v1_8B-GGUF
| null |
[
"transformers",
"gguf",
"en",
"dataset:grimulkan/theory-of-mind",
"base_model:jeiku/Average_Normie_l3_v1_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T05:27:49+00:00
|
text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.001_ablation_4iters_bs256_nodpo_sample2_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.001_ablation_4iters_bs256_nodpo_sample2_iter_1", "results": []}]}
|
ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_1
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T05:29:32+00:00
|
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