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11k
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|---|---|---|---|---|---|---|---|---|---|
jackswie/sb | jackswie | "2024-06-16T15:30:24Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-16T11:11:03Z" | ---
license: openrail
---
|
hzy88886/llama3 | hzy88886 | "2024-06-16T11:13:29Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T11:13:29Z" | ---
license: apache-2.0
---
|
AUTOMATIC/stable-diffusion-3-medium-text-encoders | AUTOMATIC | "2024-06-16T11:38:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:19:49Z" | ---
{}
---
This repository contains three text encoders and their original model card links used in [Stable Diffusion 3](https://huggingface.co/stabilityai/stable-diffusion-3-medium).
All components are subject to their respective original licenses.
CLIP-ViT/L:
* [https://huggingface.co/openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14/blob/main/README.md)
* [MIT License](https://github.com/openai/CLIP/blob/main/LICENSE)
OpenCLIP-ViT/G:
* [https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k/blob/main/README.md)
* [MIT License](https://choosealicense.com/licenses/mit)
T5 Version 1.1:
* [https://huggingface.co/google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl/blob/main/README.md)
* [Apache License 2.0](https://choosealicense.com/licenses/apache-2.0) |
Haytham-0019/H.E | Haytham-0019 | "2024-06-16T11:20:34Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T11:20:34Z" | ---
license: apache-2.0
---
|
revelacion1/q_FrozenLake_v1_4x4_noSlippery_course_base_implementation | revelacion1 | "2024-06-16T11:22:38Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-16T11:22:36Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q_FrozenLake_v1_4x4_noSlippery_course_base_implementation
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="revelacion1/q_FrozenLake_v1_4x4_noSlippery_course_base_implementation", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
revelacion1/Taxi_v3_base_course_model | revelacion1 | "2024-06-16T11:24:10Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-16T11:24:08Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi_v3_base_course_model
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="revelacion1/Taxi_v3_base_course_model", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
aibabyshark/llama38binstruct_summarize | aibabyshark | "2024-06-16T13:23:09Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | "2024-06-16T11:24:25Z" | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: llama38binstruct_summarize
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama38binstruct_summarize
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4799 | 1.25 | 25 | 1.4020 |
| 0.5231 | 2.5 | 50 | 1.5481 |
| 0.3147 | 3.75 | 75 | 1.6357 |
| 0.1531 | 5.0 | 100 | 1.7928 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
Hry62/EMNM | Hry62 | "2024-06-16T11:32:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:30:30Z" | Entry not found |
Kishor798/Llama-2-7b-chat-finetune | Kishor798 | "2024-06-16T11:38:16Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-16T11:31:41Z" | Entry not found |
wmamanee/dollLikeness | wmamanee | "2024-06-16T11:35:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:33:28Z" | Entry not found |
polyconnect/rl_course_vizdoom_health_gathering_supreme | polyconnect | "2024-06-16T15:20:23Z" | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-16T11:33:29Z" | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.91 +/- 4.75
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r polyconnect/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .home.ivan..env.u8.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .home.ivan..env.u8.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Swarts/Elraen | Swarts | "2024-06-16T14:56:00Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:35:29Z" | Entry not found |
brian-gordon/paligemma-3b-pt-448-Tasks-1-2__2024-06-16_14-35-38 | brian-gordon | "2024-06-16T11:35:40Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:35:38Z" | Entry not found |
tantb/bert-finetuned-squad-accelerate | tantb | "2024-06-16T11:36:22Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:36:22Z" | Entry not found |
aaalby/YUJINv2 | aaalby | "2024-06-16T11:40:10Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-16T11:39:06Z" | ---
license: openrail
---
|
BennieL/3d | BennieL | "2024-06-16T11:41:16Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T11:41:16Z" | ---
license: apache-2.0
---
|
manbeast3b/KinoInfer502 | manbeast3b | "2024-06-16T11:50:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T11:48:49Z" | Entry not found |
aitorrent/Meta-Llama-3-8B-Instruct-GGUF-torrent | aitorrent | "2024-06-16T11:53:35Z" | 0 | 0 | null | [
"torrent",
"license:llama3",
"region:us"
] | null | "2024-06-16T11:49:40Z" | ---
license: llama3
tags:
- torrent
---
## Llamacpp imatrix Quantizations of Meta-Llama-3-8B-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> commit <a href="https://github.com/ggerganov/llama.cpp/commit/ffe666572f98a686b17a2cd1dbf4c0a982e5ac0a">ffe6665</a> for quantization.
Original model: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Meta-Llama-3-8B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Meta-Llama-3-8B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Meta-Llama-3-8B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Meta-Llama-3-8B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Meta-Llama-3-8B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Meta-Llama-3-8B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Meta-Llama-3-8B-Instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Meta-Llama-3-8B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Meta-Llama-3-8B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Meta-Llama-3-8B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Meta-Llama-3-8B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Meta-Llama-3-8B-Instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Meta-Llama-3-8B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Meta-Llama-3-8B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Meta-Llama-3-8B-Instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Meta-Llama-3-8B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Meta-Llama-3-8B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Meta-Llama-3-8B-Instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-8B-Instruct-IQ2_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-8B-Instruct-IQ2_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-8B-Instruct-IQ1_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [Meta-Llama-3-8B-Instruct-IQ1_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-8B-Instruct-GGUF/blob/main/Meta-Llama-3-8B-Instruct-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
|
Sibinraj/T5-base-finetuned-sumx | Sibinraj | "2024-06-16T12:24:04Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-06-16T11:51:32Z" | Entry not found |
rafaeljosem/unsloth-mistral-0.3-7b-Instruct-bnb-4bit-Mexican-Laws-Inst-FineTuned-step2_2 | rafaeljosem | "2024-06-16T12:04:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T12:04:33Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** rafaeljosem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Fanjue/trained_db_output | Fanjue | "2024-06-16T12:07:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:07:11Z" | Entry not found |
krish4u/Llama-2-7b-chat-new-finetune | krish4u | "2024-06-16T12:17:19Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:12:40Z" | Entry not found |
itosve/example-model | itosve | "2024-06-16T12:37:04Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-16T12:13:40Z" | ---
license: mit
---
|
praveenmek/Llama-2-7b-chat-new-finetune | praveenmek | "2024-06-16T12:16:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:16:34Z" | Entry not found |
sarathymp/example-model | sarathymp | "2024-06-16T12:22:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:17:06Z" | #example-model
This is my model card README
---
license: mit
---
|
Aishwaryamu/Llama-2-7b-chat-new-finetune | Aishwaryamu | "2024-06-16T12:18:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:18:32Z" | Entry not found |
Pelochus/qwen2-1_5B-rk3588 | Pelochus | "2024-06-16T12:42:56Z" | 0 | 0 | null | [
"qwen2",
"rkllm",
"rockchip",
"rk3588",
"region:us"
] | null | "2024-06-16T12:19:51Z" | ---
tags:
- qwen2
- rkllm
- rockchip
- rk3588
---
# Qwen 2 1.5B for RK3588
This is a conversion from https://huggingface.co/Qwen/Qwen2-1.5B to the RKLLM format for Rockchip devices.
This runs on the NPU from the RK3588
**Latest update:** June 2024. Converted with **RKLLM runtime 1.0.1**.
# Main repo
See this for my full collection of converted LLMs for the RK3588's NPU:
https://huggingface.co/Pelochus/ezrkllm-collection
# License
Same as the original LLM https://huggingface.co/Qwen/Qwen2-1.5B |
TesHanjit/wav2vec2-base-timit-demo-google-colab | TesHanjit | "2024-06-16T12:20:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:20:08Z" | Entry not found |
SergioGreenDragon/SergioGreenDragonGenerate | SergioGreenDragon | "2024-06-16T12:26:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:26:23Z" | Entry not found |
JEFFERSONMUSIC/MJ2009era | JEFFERSONMUSIC | "2024-06-16T12:28:10Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T12:26:38Z" | ---
license: apache-2.0
---
|
RichardPhua/mbti_GPT2_model_for_JP | RichardPhua | "2024-06-16T12:26:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:26:44Z" | Entry not found |
whaohan/pivotmesh | whaohan | "2024-06-16T15:22:27Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-16T12:31:37Z" | ---
license: mit
---
|
iloncka/exp_5_old_bg_raw-subs_1_v_5_pvt_v2_b0.in1k_ep_60 | iloncka | "2024-06-16T12:33:36Z" | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | "2024-06-16T12:32:26Z" | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
satellitegazor/Llama-2-7b-chat-new-finetune | satellitegazor | "2024-06-16T12:40:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:35:27Z" | Entry not found |
skkjodhpur/Llama-2-7b-chat-new-finetune | skkjodhpur | "2024-06-16T12:35:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:35:34Z" | Entry not found |
PB7-DUT-2023/peft_Mistral_7b_v3 | PB7-DUT-2023 | "2024-06-16T12:38:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T12:38:31Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sharad31/lora_model | sharad31 | "2024-06-16T12:38:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T12:38:43Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** sharad31
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
iloncka/exp_5_old_bg_raw-subs_1_v_5_convnextv2_pico.fcmae_ft_in1k_ep_60 | iloncka | "2024-06-16T12:42:09Z" | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | "2024-06-16T12:40:58Z" | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Frixi/Juliaaab | Frixi | "2024-06-16T12:43:44Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-16T12:43:22Z" | ---
license: openrail
---
|
alexgambashidze/SDXL_NCP-DPO_v0.1 | alexgambashidze | "2024-06-29T09:45:28Z" | 0 | 7 | null | [
"arxiv:2406.17636",
"region:us"
] | null | "2024-06-16T12:43:33Z" | ---
{}
---
This model card contains trained SDXL LoRA weights using [Noise-Conditioned Perceptual Preference Optimization](https://arxiv.org/pdf/2406.17636).
Combining Noise-Conditioned Perception with DPO significantly outperforms baseline DPO method in both training speed and overall quality measured by human preferences.
We publish LoRA weights that are trained for 10 H100 GPU hours on a small subset of a Pick-a-Picv2 dataset. We removed all non-absolute winners for each prompt and our final prompts and image ids can be found [here](https://drive.google.com/file/d/1q1fvItyLq2YI_O6oNLyNYiB9opeZLYdB/view?usp=sharing)
training code: https://github.com/sakharok13/Aligning-Stable-Diffusion-with-Noise-Conditioned-Perception
To try our LoRA weights and compare with a baseline:
```python
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
pipe.to("cuda")
prompt = "An astronaut riding a green horse"
torch.manual_seed(10)
original_image = pipe(prompt=prompt).images[0]
pipe.load_lora_weights("alexgambashidze/SDXL_NCP-DPO_v0.1", weight_name="pytorch_lora_weights.safetensors")
torch.manual_seed(10)
ncp_dpo_image = pipe(prompt=prompt).images[0]
```
Limitations: Pick-a-Picv2 dataset is extremely biased. It contains NSFW generations and is focused on people & characters.
Core contributors:
Alexander Gambashidze
Yuri Sosnin
Anton Kulikov |
npsai/Llama-2-7b-chat-new-finetune | npsai | "2024-06-16T12:45:16Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:45:16Z" | Entry not found |
KANISHKVIJAY/gpt2_interview_model | KANISHKVIJAY | "2024-06-16T12:57:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-16T12:46:56Z" | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: gpt2_interview_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_interview_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2856
## 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.7075 | 0.0980 | 5 | 3.2856 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
A01705317/results | A01705317 | "2024-06-16T12:56:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:56:26Z" | Entry not found |
Mashengshuaiqi/result | Mashengshuaiqi | "2024-06-16T13:15:49Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-06-16T12:57:15Z" | Entry not found |
z41285379/wang_miku_dreambooth | z41285379 | "2024-06-16T12:58:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T12:58:26Z" | Entry not found |
geraldabrhm/llama-2-13b-pcl-all-augment-capital-label | geraldabrhm | "2024-06-17T05:09:44Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-16T12:59:46Z" | Entry not found |
generaptor/slnsw-image-summary | generaptor | "2024-06-16T13:01:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:01:55Z" | Entry not found |
narsimhaMurthy/QMSGPT | narsimhaMurthy | "2024-06-16T13:02:55Z" | 0 | 0 | null | [
"license:llama2",
"region:us"
] | null | "2024-06-16T13:02:55Z" | ---
license: llama2
---
|
OreX/Automatic1111 | OreX | "2024-06-29T17:31:16Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:03:13Z" | Entry not found |
nyu-visionx/cambrian-13b-pretrain | nyu-visionx | "2024-06-16T13:12:23Z" | 0 | 1 | null | [
"region:us"
] | null | "2024-06-16T13:08:05Z" | Entry not found |
nyu-visionx/cambrian-8b-pretrain | nyu-visionx | "2024-06-16T13:11:58Z" | 0 | 1 | null | [
"region:us"
] | null | "2024-06-16T13:08:17Z" | Entry not found |
DinaZahran/Phi-3-tuned-pandas | DinaZahran | "2024-06-17T12:28:39Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:08:27Z" | Entry not found |
nyu-visionx/cambrian-34b-pretrain | nyu-visionx | "2024-06-16T13:12:53Z" | 0 | 1 | null | [
"region:us"
] | null | "2024-06-16T13:08:37Z" | Entry not found |
PenelopeSystems/penelope-palette | PenelopeSystems | "2024-06-16T16:45:38Z" | 0 | 7 | null | [
"en",
"arxiv:2403.03206",
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T13:09:15Z" | ---
license: apache-2.0
language:
- en
---
# Penelope Palette: Portrait Generation Model
Important note : Provisory Model card mostly a placeholder.
## Model Description
Penelope Palette is an advanced AI model designed for creating lifelike portraits. It leverages the same architecture as Stable Diffusion 3, ensuring high-quality image generation with remarkable detail and style. Most of the description was copied from the stable diffusion 3 since the informations remains generally the same.
The model is weaker than Stable Diffusion 3 medium , having trouble generating realistic content ; nudity and anatomy but it performs really good in portraits , having a unique style .
## Model Description
Developed by: Penelope Systems
Model type: MMDiT text-to-image generative model
Model Description: This is a model that can be used to generate images based on text prompts. It is a Multimodal Diffusion Transformer (https://arxiv.org/abs/2403.03206) that uses three fixed, pretrained text encoders (OpenCLIP-ViT/G, CLIP-ViT/L and T5-xxl)
# License
Apache llicense 2.0
# Model Sources
For local or self-hosted use, we recommend ComfyUI for inference.
It has built-in clip so it shoul be plug & play .
ComfyUI: https://github.com/comfyanonymous/ComfyUI
# Training Dataset
We used synthetic data and filtered publicly available data to train our models. The model was pre-trained on 1 billion images. The fine-tuning data includes 30M high-quality aesthetic images focused on specific visual content and style, as well as 3M preference data images.
# Uses
Intended Uses
Intended uses include the following:
Generation of artworks and use in design and other artistic processes.
Applications in educational or creative tools.
Research on generative models, including understanding the limitations of generative models.
# Out-of-Scope Uses
The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
# Safety
Same safety measures used by Stable Diffusion 3 were deployed .
# Use recommendations :
For best use we recommand :
- steps : 32
- cfg : between 4.0 and 7.0
- sampler_name : dpmpp_2m
- scheduler : sgm_uniform
# # For best generations don't try to use realism | use words like "portrait" ; "art" ; "sketch" and so on .
 |
LuuNgoc2k2/bartpho-word-base-cp1 | LuuNgoc2k2 | "2024-06-16T13:12:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:12:46Z" | Entry not found |
aitorrent/Meta-Llama-3-70B-Instruct-GGUF-torrent | aitorrent | "2024-06-16T13:32:57Z" | 0 | 0 | null | [
"torrent",
"license:llama3",
"region:us"
] | null | "2024-06-16T13:13:11Z" | ---
license: llama3
tags:
- torrent
---
## Llamacpp imatrix Quantizations of Meta-Llama-3-70B-Instruct
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2777">b2777</a> for quantization.
Original model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Meta-Llama-3-70B-Instruct-Q8_0.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/tree/main/Meta-Llama-3-70B-Instruct-Q8_0.gguf) | Q8_0 | 74.97GB | Extremely high quality, generally unneeded but max available quant. |
| [Meta-Llama-3-70B-Instruct-Q6_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/tree/main/Meta-Llama-3-70B-Instruct-Q6_K.gguf) | Q6_K | 57.88GB | Very high quality, near perfect, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q5_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q5_K_M.gguf) | Q5_K_M | 49.94GB | High quality, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q5_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q5_K_S.gguf) | Q5_K_S | 48.65GB | High quality, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q4_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q4_K_M.gguf) | Q4_K_M | 42.52GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q4_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q4_K_S.gguf) | Q4_K_S | 40.34GB | Slightly lower quality with more space savings, *recommended*. |
| [Meta-Llama-3-70B-Instruct-IQ4_NL.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ4_NL.gguf) | IQ4_NL | 40.05GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Meta-Llama-3-70B-Instruct-IQ4_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ4_XS.gguf) | IQ4_XS | 37.90GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Meta-Llama-3-70B-Instruct-Q3_K_L.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_L.gguf) | Q3_K_L | 37.14GB | Lower quality but usable, good for low RAM availability. |
| [Meta-Llama-3-70B-Instruct-Q3_K_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_M.gguf) | Q3_K_M | 34.26GB | Even lower quality. |
| [Meta-Llama-3-70B-Instruct-IQ3_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_M.gguf) | IQ3_M | 31.93GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Meta-Llama-3-70B-Instruct-IQ3_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_S.gguf) | IQ3_S | 30.91GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Meta-Llama-3-70B-Instruct-Q3_K_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q3_K_S.gguf) | Q3_K_S | 30.91GB | Low quality, not recommended. |
| [Meta-Llama-3-70B-Instruct-IQ3_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_XS.gguf) | IQ3_XS | 29.30GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Meta-Llama-3-70B-Instruct-IQ3_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ3_XXS.gguf) | IQ3_XXS | 27.46GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Meta-Llama-3-70B-Instruct-Q2_K.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-Q2_K.gguf) | Q2_K | 26.37GB | Very low quality but surprisingly usable. |
| [Meta-Llama-3-70B-Instruct-IQ2_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ2_M.gguf) | IQ2_M | 24.11GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Meta-Llama-3-70B-Instruct-IQ2_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ2_S.gguf) | IQ2_S | 22.24GB | Very low quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-70B-Instruct-IQ2_XS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ2_XS.gguf) | IQ2_XS | 21.14GB | Very low quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-70B-Instruct-IQ2_XXS.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ2_XXS.gguf) | IQ2_XXS | 19.09GB | Lower quality, uses SOTA techniques to be usable. |
| [Meta-Llama-3-70B-Instruct-IQ1_M.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ1_M.gguf) | IQ1_M | 16.75GB | Extremely low quality, *not* recommended. |
| [Meta-Llama-3-70B-Instruct-IQ1_S.gguf](https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF/blob/main/Meta-Llama-3-70B-Instruct-IQ1_S.gguf) | IQ1_S | 15.34GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Meta-Llama-3-70B-Instruct-GGUF --include "Meta-Llama-3-70B-Instruct-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Meta-Llama-3-70B-Instruct-GGUF --include "Meta-Llama-3-70B-Instruct-Q8_0.gguf/*" --local-dir Meta-Llama-3-70B-Instruct-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (Meta-Llama-3-70B-Instruct-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
|
Poloikon/Duble | Poloikon | "2024-06-16T13:13:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:13:44Z" | Entry not found |
rejauldu/Llama-3-8B-Instruct | rejauldu | "2024-06-17T04:14:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:18:18Z" | Entry not found |
richardkelly/Qwen-Qwen1.5-0.5B-1718543909 | richardkelly | "2024-06-16T13:18:34Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-06-16T13:18:29Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
# Model Card for Model ID
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richardkelly/Qwen-Qwen1.5-0.5B-1718543940 | richardkelly | "2024-06-16T13:19:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-06-16T13:19:01Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
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richardkelly/Qwen-Qwen1.5-1.8B-1718543969 | richardkelly | "2024-06-16T13:19:35Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-06-16T13:19:30Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-1.8B
---
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wave-on-discord/gemini-nano | wave-on-discord | "2024-06-24T15:44:06Z" | 0 | 91 | null | [
"region:us"
] | null | "2024-06-16T13:19:53Z" | gemini nano extracted from chrome
loadable through [mediapipe](https://github.com/google-ai-edge/mediapipe), ~~but it's a base model so it won't be very coherent~~. maybe try applying the [adapter](https://huggingface.co/wave-on-discord/gemini-nano-adapter)?
update: looks like this one has some instruction tuning. idk what the adapter is for then |
richardkelly/Qwen-Qwen1.5-1.8B-1718544001 | richardkelly | "2024-06-16T13:20:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-06-16T13:20:01Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-1.8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[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
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[More Information Needed]
## Training Details
### Training Data
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richardkelly/Qwen-Qwen1.5-7B-1718544099 | richardkelly | "2024-06-16T13:21:48Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-06-16T13:21:39Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-7B
---
# Model Card for Model ID
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richardkelly/Qwen-Qwen1.5-7B-1718544132 | richardkelly | "2024-06-16T13:22:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"region:us"
] | null | "2024-06-16T13:22:12Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-7B
---
# Model Card for Model ID
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- PEFT 0.11.1 |
tictactoe1/llama3_medical_model | tictactoe1 | "2024-06-16T13:22:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T13:22:24Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** tictactoe1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rbn2008k/chatbot | rbn2008k | "2024-06-16T13:22:38Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-16T13:22:38Z" | ---
license: mit
---
|
richardkelly/google-gemma-2b-1718544160 | richardkelly | "2024-06-16T13:22:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | "2024-06-16T13:22:40Z" | ---
library_name: peft
base_model: google/gemma-2b
---
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richardkelly/google-gemma-2b-1718544191 | richardkelly | "2024-06-16T13:23:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"region:us"
] | null | "2024-06-16T13:23:12Z" | ---
library_name: peft
base_model: google/gemma-2b
---
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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richardkelly/google-gemma-7b-1718544280 | richardkelly | "2024-06-16T13:25:03Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"region:us"
] | null | "2024-06-16T13:24:40Z" | ---
library_name: peft
base_model: google/gemma-7b
---
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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richardkelly/google-gemma-7b-1718544313 | richardkelly | "2024-06-16T13:25:37Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-7b",
"region:us"
] | null | "2024-06-16T13:25:13Z" | ---
library_name: peft
base_model: google/gemma-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
nlpllm007/assignment_t5_translation | nlpllm007 | "2024-06-16T13:43:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:43:59Z" | Entry not found |
ygohel18/rvc-male-veda | ygohel18 | "2024-06-16T13:50:55Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-16T13:50:55Z" | ---
license: gpl-3.0
---
|
anon11112/onepunch | anon11112 | "2024-06-16T13:56:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:54:10Z" | Entry not found |
quangtqv/cross_encoder_tool_learning_test_16_6 | quangtqv | "2024-06-16T13:57:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-16T13:54:42Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Model Card Contact
[More Information Needed] |
wave-on-discord/gemini-nano-adapter | wave-on-discord | "2024-06-24T15:44:28Z" | 0 | 19 | null | [
"tflite",
"region:us"
] | null | "2024-06-16T13:55:24Z" | gemini nano adapter extracted from chrome. unknown purpose |
anon11112/idk | anon11112 | "2024-06-16T13:57:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:57:19Z" | Entry not found |
hishamcse/Reinforce-CartPole-v1 | hishamcse | "2024-06-16T15:59:59Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-16T13:58:01Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 5000.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
anon11112/elf | anon11112 | "2024-06-16T13:58:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:58:27Z" | Entry not found |
anon11112/aoi | anon11112 | "2024-06-16T13:59:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:59:02Z" | Entry not found |
aitorrent/dolphin-2.9.3-qwen2-0.5b-GGUF-torrent | aitorrent | "2024-06-16T14:09:48Z" | 0 | 0 | null | [
"torrent",
"license:apache-2.0",
"region:us"
] | null | "2024-06-16T13:59:12Z" | ---
license: apache-2.0
tags:
- torrent
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-0.5b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-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/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_S.gguf) | IQ3_S | 0.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_XS.gguf) | IQ3_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ3_M.gguf) | IQ3_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-0.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-0.5b.f16.gguf) | f16 | 1.1 | 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 |
dae1337/epikhaxor | dae1337 | "2024-06-16T13:59:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T13:59:53Z" | Entry not found |
serhiipas/peft-starcoder-lora-a100 | serhiipas | "2024-06-16T14:05:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:05:56Z" | Entry not found |
richardkelly/Qwen-Qwen1.5-0.5B-1718546901 | richardkelly | "2024-06-16T14:08:27Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-06-16T14:08:22Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-0.5B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
axssel/linda_caicedo | axssel | "2024-06-16T16:17:39Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:09:28Z" | Entry not found |
vaniiiii/vani_dataset | vaniiiii | "2024-06-17T07:07:10Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-16T14:09:55Z" | Entry not found |
aitorrent/dolphin-2.9.3-qwen2-1.5b-GGUF | aitorrent | "2024-06-16T14:21:07Z" | 0 | 0 | transformers | [
"transformers",
"generated_from_trainer",
"axolotl",
"torrent",
"en",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:cognitivecomputations/dolphin-2.9.3-qwen2-1.5b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T14:15:13Z" | ---
base_model: cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
- axolotl
- torrent
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-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/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.IQ3_XS.gguf) | IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.IQ3_S.gguf) | IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.IQ3_M.gguf) | IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-qwen2-1.5b-GGUF/resolve/main/dolphin-2.9.3-qwen2-1.5b.f16.gguf) | f16 | 3.2 | 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
<!-- end --> |
cliq/my-cool-model | cliq | "2024-06-16T14:17:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:17:09Z" | Entry not found |
jhj0517/whisper.cpp | jhj0517 | "2024-06-16T14:19:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:19:23Z" | Entry not found |
bisratwalle/Amharic-llama-for-family-code-of-ethiopia-v0.1.gguf | bisratwalle | "2024-06-16T14:24:57Z" | 0 | 0 | null | [
"gguf",
"region:us"
] | null | "2024-06-16T14:19:28Z" | Entry not found |
youcaihua/forget_llama | youcaihua | "2024-06-17T00:18:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | "2024-06-16T14:23:01Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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anon11112/megumin | anon11112 | "2024-06-16T14:26:57Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:26:36Z" | Entry not found |
axssel/alec_bohm | axssel | "2024-06-16T14:27:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:27:38Z" | Entry not found |
zhan1993/private_phi2_256clusters_by_embedding_ep5 | zhan1993 | "2024-06-17T12:30:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:29:40Z" | Number of experts present in the library: 256
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| cluster_239 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_48 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_98 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_158 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_172 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_55 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_118 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_109 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_14 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_153 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_83 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_56 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_141 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_155 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_85 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_114 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_181 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_3 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_39 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_175 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_103 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_202 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_142 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_91 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_234 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_221 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_37 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_67 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_170 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_27 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_146 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_69 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_183 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_251 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_148 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_241 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_122 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_21 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_232 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_200 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_12 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_77 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_206 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_154 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_235 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_110 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_229 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_72 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_171 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_165 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_209 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_218 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_169 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_162 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_24 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_33 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_152 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_203 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_86 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_29 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_66 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_131 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_42 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_247 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_46 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_49 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_96 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_93 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_134 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_176 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_207 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_79 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_197 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_178 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_163 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_143 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_52 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_31 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_133 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_59 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_105 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_113 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_194 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_159 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_139 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_57 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_92 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_231 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_225 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_242 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_145 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_166 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_117 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_237 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_74 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_227 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_90 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_87 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_222 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_190 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_106 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_15 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_137 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_161 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_94 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_71 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_253 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_199 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_23 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_184 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_51 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_130 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_160 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_115 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_204 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_157 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_255 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_95 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_65 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_6 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_213 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_47 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_97 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_11 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_100 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_249 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_121 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_179 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_144 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_80 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_187 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_128 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_5 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_167 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_189 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_230 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_18 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_136 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_180 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_82 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_40 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_4 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_38 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_243 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_8 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_226 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_41 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_89 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_201 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_2 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_1 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_125 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_156 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_17 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_119 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_246 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_208 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_123 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_135 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_186 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_101 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_215 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_107 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_104 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_214 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_60 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_88 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_112 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_177 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_116 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_228 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_45 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_120 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_240 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_102 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_210 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_195 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_244 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_19 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_10 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_140 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_151 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_0 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_138 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
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| cluster_149 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_111 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_182 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_30 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_76 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_58 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_236 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_216 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_129 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_34 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_192 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_254 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_233 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_84 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_68 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_164 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_238 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_132 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_196 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_126 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_150 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_185 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_63 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_245 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_99 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_70 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_252 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_9 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_188 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_248 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_220 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_127 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_124 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_62 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_54 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_28 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_173 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_211 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_50 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_212 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_26 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_168 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_20 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_174 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_219 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_22 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
| cluster_224 | phi-2 | zhan1993/flan-10k-flat-cluster-embedding/None | lora |
Last updated on: 2024-06-17 10:12:46+00:00
|
cackerman/llama2_13b_chat_unpaired_projection_tune_out | cackerman | "2024-06-16T14:40:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-16T14:32:40Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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### Results
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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knowingpearl/RRATheReactRespondAlgorithm | knowingpearl | "2024-06-16T14:42:24Z" | 0 | 0 | null | [
"not-for-all-audiences",
"question-answering",
"en",
"dataset:HuggingFaceFW/fineweb",
"dataset:HuggingFaceFW/fineweb-edu",
"dataset:TIGER-Lab/MMLU-Pro",
"dataset:NousResearch/CharacterCodex",
"dataset:openbmb/RLAIF-V-Dataset",
"dataset:TIGER-Lab/WebInstructSub",
"dataset:Locutusque/function-calling-chatml",
"dataset:UCSC-VLAA/Recap-DataComp-1B",
"dataset:OpenGVLab/ShareGPT-4o",
"dataset:ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions",
"license:unknown",
"region:us"
] | question-answering | "2024-06-16T14:34:24Z" | ---
license: unknown
datasets:
- HuggingFaceFW/fineweb
- HuggingFaceFW/fineweb-edu
- TIGER-Lab/MMLU-Pro
- NousResearch/CharacterCodex
- openbmb/RLAIF-V-Dataset
- TIGER-Lab/WebInstructSub
- Locutusque/function-calling-chatml
- UCSC-VLAA/Recap-DataComp-1B
- OpenGVLab/ShareGPT-4o
- ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions
language:
- en
metrics:
- accuracy
- bertscore
- bleu
- bleurt
- brier_score
- cer
- character
- charcut_mt
- chrf
- code_eval
pipeline_tag: question-answering
tags:
- not-for-all-audiences
--- |
aitorrent/Meta-Llama-3-70B-Instruct-abliterated-v3.5-GGUF-torrent | aitorrent | "2024-06-16T14:35:34Z" | 0 | 0 | transformers | [
"transformers",
"torrent",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | "2024-06-16T14:34:33Z" | ---
library_name: transformers
license: llama3
tags:
- torrent
---
# Llama-3-70B-Instruct-abliterated-v3.5 Model Card
[My original Jupyter "cookbook" to replicate the methodology can be found here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
[My personal library o' code used](https://github.com/FailSpy/abliterator) (WIP, looking to improve and generalize)
This is [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
## V3.5?
Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P
This one has only a single layer modified(!) and that seems to have greatly reduced moralizing disclaimers.
I hope you'll find this model better than 70B-V3! As well, this also fixes the tokenizer.
## Hang on, "abliteration"? Orthogonalization? Ablation? What is this?
TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out.
**TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes.
Ablate + obliterated = Abliterated
Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization.
## A little more on the methodology, and why this is interesting
To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt.
Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights.
> Why this over fine-tuning?
Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage.
As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.)
Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques.
It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa.
I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity.
> Okay, fine, but why V3? There's no V2 70B?
Well, I released a V2 a while back for 8B under Cognitive Computations.
It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model.
I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations.
So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's *such* an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.)
## Quirkiness awareness notice
This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects.
If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can. |
richardkelly/Qwen-Qwen1.5-1.8B-1718548509 | richardkelly | "2024-06-16T14:35:15Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-06-16T14:35:10Z" | ---
library_name: peft
base_model: Qwen/Qwen1.5-1.8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.11.1 |
hngan/halpee | hngan | "2024-06-16T14:47:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-16T14:36:02Z" | Entry not found |
Dharinesh/finetuned-gemma-function-calling | Dharinesh | "2024-06-16T14:39:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-16T14:36:54Z" | ---
library_name: transformers
tags:
- trl
- sft
---
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