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6.53k
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imagepipeline/NegativeXL | imagepipeline | "2024-04-10T22:18:41Z" | 0 | 0 | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | "2024-04-10T22:18:37Z" | ---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## NegativeXL
<img src="" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This embedding model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details -
[](https://imagepipeline.io/models/NegativeXL?id=39700980-3d4f-49cf-a6f5-11eceebb9861/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "sdxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "39700980-3d4f-49cf-a6f5-11eceebb9861",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
imagepipeline/SimplePositiveXL | imagepipeline | "2024-04-10T22:19:25Z" | 0 | 0 | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | "2024-04-10T22:19:23Z" | ---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## SimplePositiveXL
<img src="" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This embedding model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details -
[](https://imagepipeline.io/models/SimplePositiveXL?id=4581d8f3-14c8-4015-8d09-d262ce51ccec/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "sdxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "4581d8f3-14c8-4015-8d09-d262ce51ccec",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
coivmn/keitasinging | coivmn | "2024-04-10T22:23:47Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-10T22:22:57Z" | ---
license: openrail
---
|
IamYash/VA-LLM-2rwul3je | IamYash | "2024-04-10T22:23:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:23:07Z" | Entry not found |
ArpithaSriAI/Mistral7bFinetuned | ArpithaSriAI | "2024-04-10T22:23:13Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-10T22:23:12Z" | ---
license: apache-2.0
---
|
peterface/lora_model_v35 | peterface | "2024-04-10T22:47:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T22:23:42Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** peterface
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-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)
|
zwellington/azahead-longformer-v1.4098 | zwellington | "2024-04-10T22:25:22Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:25:20Z" | Entry not found |
Tristan/pythia-410m-deduped-test-upsampled | Tristan | "2024-04-10T23:12:10Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-04-10T22:27:39Z" | Entry not found |
menagiemrah/Antonio-VoiceClone | menagiemrah | "2024-04-12T16:27:16Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:30:21Z" | Entry not found |
oribrand/TinyLlama-1.1B-Chat-v1.0-sw | oribrand | "2024-04-10T23:12:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T22:31:31Z" | Entry not found |
prathishpratt/git_sketch | prathishpratt | "2024-04-10T22:32:14Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-10T22:32:11Z" | ---
license: apache-2.0
---
|
IamYash/VA-LLM-0nlb1p7c | IamYash | "2024-04-10T22:46:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:32:57Z" | Entry not found |
Sasha177117/Sasha177117 | Sasha177117 | "2024-04-10T22:35:30Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:35:30Z" | Entry not found |
oneandahalfcats/7758 | oneandahalfcats | "2024-04-10T22:36:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:36:00Z" | Entry not found |
mgomez/MedPaxTral-2x7b | mgomez | "2024-04-10T22:37:25Z" | 0 | 0 | transformers | [
"transformers",
"mixtral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T22:37:23Z" | Entry not found |
0x0uncle0/aunt46 | 0x0uncle0 | "2024-04-10T22:40:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T22:38:36Z" | Entry not found |
hvein/moon525 | hvein | "2024-04-12T04:23:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:39:02Z" | Entry not found |
Gusanidas/trilis_d2ln | Gusanidas | "2024-04-10T23:19:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T22:40:23Z" | Entry not found |
0x0uncle0/aunt47 | 0x0uncle0 | "2024-04-10T22:41:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T22:40:38Z" | Entry not found |
mscheny/mine3_17 | mscheny | "2024-06-03T07:14:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:41:09Z" | Entry not found |
IamYash/VA-LLM-iw1zaiof | IamYash | "2024-04-10T22:42:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:42:00Z" | Entry not found |
Weni/WeniGPT-Agents-Mistral-1.0.1-SFT-AWQ | Weni | "2024-04-10T23:06:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | "2024-04-10T22:42:58Z" | Entry not found |
RobotSail/merlinite-7b | RobotSail | "2024-04-10T22:45:58Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-10T22:45:58Z" | ---
license: apache-2.0
---
|
oneandahalfcats/7487 | oneandahalfcats | "2024-04-10T22:48:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:47:56Z" | Entry not found |
nghiemhnlp/HateCOT_Llama_13B | nghiemhnlp | "2024-04-10T22:49:08Z" | 0 | 0 | peft | [
"peft",
"hatespeech",
"hatecot",
"cot",
"llama",
"text-classification",
"arxiv:2403.11456",
"license:apache-2.0",
"region:us"
] | text-classification | "2024-04-10T22:48:04Z" | ---
library_name: peft
license: apache-2.0
pipeline_tag: text-classification
tags:
- hatespeech
- hatecot
- cot
- llama
---
## Introduction
This is the LoRA-adapater for the Llama-13B introduced in the paper
*HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models*.
The base model is instruction-finetuned on 52,000 samples that includes augmented humman annotation to produce
legible explanations based on predefined criteria in the **provided definition**.
To use the model, please load along with the original Llama model (detailed configuration in the *Training Procedure*).
For instruction to load Peft models: https://huggingface.co/docs/transformers/main/en/peft
These adapters can also be finetuned on a new set of data. See the article for more details.
## Usage
Use the following template to prompt the model:
```
### Instruction
Perform this task by considering the following Definitions.
Based on the message, label the input as only one of the following categories:
[Class 1], [Class 2], ..., or [Class N].
Provide a brief paragraph to explain step-by-step why the post should be classsified
with the provided Label based on the given Definitions. If this post targets a group or
entity relevant to the definition of the specified Label, explain who this target is and how
that leads to that Label.
Append the string '<END>' to the end of your response. Provide your response in the following format:
EXPLANATION: [text]
LABEL:[text] <END>
### Definitions:
[Class 1]: [Definition 1]
[Class 2]: [Definition 2]
...
[Class N]: [Definition 3]
### Input
{post}
### Response:
```
## Citation
```bibtex
@article{nghiem2024hatecot,
title={HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models},
author={Nghiem, Huy and Daum{\'e} III, Hal},
journal={arXiv preprint arXiv:2403.11456},
year={2024}
}
```
## Original Model
Please visit the main repository to gain permission to download original model weights.
https://huggingface.co/meta-llama
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0 |
spxrks3x/Isshiki | spxrks3x | "2024-04-10T22:48:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:48:04Z" | Entry not found |
florianhoenicke/pet-shop-100-64-20-BAAI_bge-small-en-v1.5_9062874564 | florianhoenicke | "2024-04-10T22:48:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:48:23Z" | # pet-shop-100-64-20-BAAI_bge-small-en-v1.5_9062874564
## Model Description
pet-shop-100-64-20-BAAI_bge-small-en-v1.5_9062874564 is a fine-tuned version of BAAI/bge-small-en-v1.5 designed for a specific domain.
## Use Case
This model is designed to support various applications in natural language processing and understanding.
## Associated Dataset
This the dataset for this model can be found [**here**](https://huggingface.co/datasets/florianhoenicke/pet-shop-100-64-20-BAAI_bge-small-en-v1.5_9062874564).
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from transformers import AutoModel, AutoTokenizer
llm_name = "pet-shop-100-64-20-BAAI_bge-small-en-v1.5_9062874564"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
|
Linkario/Spooky-Month | Linkario | "2024-10-12T21:22:12Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-10T22:49:44Z" | ---
license: openrail
---
|
nemosene/fooocus-models | nemosene | "2024-04-10T23:25:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T22:52:47Z" | Entry not found |
IMENMANSOUR/q-FrozenLake-v1-4x4-noSlippery | IMENMANSOUR | "2024-04-10T22:55:59Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-10T22:53:16Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="IMENMANSOUR/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
florianhoenicke/pet-shop-100-64-10-jinaai_jina-embeddings-v2-small-en_9062874564 | florianhoenicke | "2024-04-11T11:54:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"custom_code",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-04-10T22:56:09Z" | # pet-shop-100-64-10-jinaai_jina-embeddings-v2-small-en_9062874564
## Model Description
pet-shop-100-64-10-jinaai_jina-embeddings-v2-small-en_9062874564 is a fine-tuned version of jinaai/jina-embeddings-v2-small-en designed for a specific domain.
## Use Case
This model is designed to support various applications in natural language processing and understanding.
## Associated Dataset
This the dataset for this model can be found [**here**](https://huggingface.co/datasets/florianhoenicke/pet-shop-100-64-10-jinaai_jina-embeddings-v2-small-en_9062874564).
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from transformers import AutoModel, AutoTokenizer
llm_name = "pet-shop-100-64-10-jinaai_jina-embeddings-v2-small-en_9062874564"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
|
IMENMANSOUR/TAXI-V3 | IMENMANSOUR | "2024-04-10T23:00:09Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-10T23:00:07Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TAXI-V3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 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="IMENMANSOUR/TAXI-V3", 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"])
```
|
prabhu194/mistralai-Code-Instruct-Finetune-test | prabhu194 | "2024-04-10T23:00:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T23:00:11Z" | Entry not found |
suneeln-duke/squad-qa-ft-falcon | suneeln-duke | "2024-04-10T23:52:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:01:54Z" | ---
library_name: transformers
tags: []
---
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tomaszki/stablelm-23-a | tomaszki | "2024-04-10T23:06:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:05:26Z" | ---
library_name: transformers
tags: []
---
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Unblocked/Mistral-7b-v0.2-unblocked-peft-v7 | Unblocked | "2024-04-10T23:07:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:07:08Z" | ---
library_name: transformers
tags: []
---
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YHLam/fix_for_completed_by | YHLam | "2024-04-10T23:12:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:07:48Z" | Entry not found |
adamo1139/Yi-34B-200K-AEZAKMI-RAW-TOXIC-XLCTX-1004-LoRA | adamo1139 | "2024-05-27T21:36:24Z" | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-04-10T23:11:42Z" | ---
license: apache-2.0
---
Merge LoRAs one after another.
Last adapter is from training on toxic-natural-v4 dataset, model seems nice so far. |
akrimi11/donut-cord | akrimi11 | "2024-04-10T23:31:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:12:27Z" | ---
library_name: transformers
tags: []
---
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michaelw37/s38 | michaelw37 | "2024-04-10T23:17:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:15:13Z" | ---
library_name: transformers
tags: []
---
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Gusanidas/triCsol_d | Gusanidas | "2024-04-11T01:34:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:15:49Z" | Entry not found |
Rayrayyy/llama2-qlora-finetuned-multilingual | Rayrayyy | "2024-04-10T23:19:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:16:09Z" | ---
library_name: transformers
tags: []
---
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[More Information Needed]
|
hui168/ppo-LunarLander-v2-from-scratch | hui168 | "2024-04-12T23:35:16Z" | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-10T23:16:57Z" | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 109.96 +/- 50.36
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 3000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 2048
'anneal_lr': False
'gae': True
'gamma': 0.9999
'gae_lambda': 0.98
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'hui168/ppo-LunarLander-v2-from-scratch'
'batch_size': 8192
'minibatch_size': 2048}
```
|
hojjatazimi/decnef_fmri | hojjatazimi | "2024-04-10T23:18:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T23:18:20Z" | Entry not found |
jhonatanoliveira/pokemon-lora | jhonatanoliveira | "2024-04-10T23:19:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T23:19:30Z" | Entry not found |
Coolwowsocoolwow/Freddy_FHSZ3RO | Coolwowsocoolwow | "2024-04-10T23:25:43Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-10T23:23:35Z" | ---
license: openrail
---
|
Ying7888/bert-lora-ar-training-1712791540 | Ying7888 | "2024-04-11T10:35:15Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-04-10T23:25:50Z" | Entry not found |
Iniyavanr11/Ini | Iniyavanr11 | "2024-04-10T23:29:54Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-04-10T23:27:05Z" | ---
license: apache-2.0
---
|
CodeJesus77/mistralprodfanyiV3 | CodeJesus77 | "2024-04-11T06:19:52Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-04-10T23:28:08Z" | Entry not found |
Asis41/FineTuningMistral_V2 | Asis41 | "2024-04-10T23:28:31Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-04-10T23:28:31Z" | ---
license: unknown
---
|
oneandahalfcats/1168 | oneandahalfcats | "2024-04-10T23:28:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T23:28:47Z" | Entry not found |
tingting/openai-whisper-large-v2-common_voice_11_12_13_14_15_16_161-LORA | tingting | "2024-04-10T23:33:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:32:59Z" | ---
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.
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- **Hardware Type:** [More Information Needed]
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Cloudxego/Jin | Cloudxego | "2024-08-21T00:33:18Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-10T23:35:38Z" | ---
license: openrail
---
|
Grayx/unstable_72 | Grayx | "2024-04-11T02:16:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:42:12Z" | ---
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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MTuan/Mi | MTuan | "2024-04-10T23:43:29Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-10T23:43:25Z" | Entry not found |
elyadenysova/falcon-7b-sileod | elyadenysova | "2024-04-17T15:51:38Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-04-10T23:46:14Z" | Entry not found |
Rhaps360/opt125m-ins-ft | Rhaps360 | "2024-04-11T14:11:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:46:15Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoTokenizer, pipeline
import torch
model = "Rhaps360/opt125m-ins-ft"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cpu"
)
messages = [
{"role": "user",
"content": "you are a poet who can write poem on machine learning",
"text":"write a poem on machine learning"}
]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(
prompt,
max_new_tokens=200,
do_sample=True,
temperature=0.00001,
top_k=50,
top_p=0.95,
repetition_penalty=20.0
)
print(outputs[0]["generated_text"][len(prompt):])
``` |
0x0uncle0/uncle02 | 0x0uncle0 | "2024-04-11T00:01:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:51:52Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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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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LongQ/Mistral_SFT_Lora | LongQ | "2024-04-11T05:09:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-10T23:56:12Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mkiku2896/gpt_tiny_mixtral_ja_openassistant | mkiku2896 | "2024-04-10T23:58:10Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:57:03Z" | ---
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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- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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#### Testing Data
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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]
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michaelw37/su7 | michaelw37 | "2024-04-11T00:01:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-10T23:58:52Z" | ---
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. -->
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[More Information Needed]
### Out-of-Scope Use
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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
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. -->
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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92amartins/llama-2-7b-10s | 92amartins | "2024-04-11T00:00:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:00:50Z" | Entry not found |
Rud/bigbird_lora_multi_lexsum_bfloat16 | Rud | "2024-04-11T00:05:50Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/bigbird-pegasus-large-bigpatent",
"base_model:adapter:google/bigbird-pegasus-large-bigpatent",
"license:apache-2.0",
"region:us"
] | null | "2024-04-11T00:05:45Z" | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: google/bigbird-pegasus-large-bigpatent
metrics:
- rouge
model-index:
- name: bigbird_lora_multi_lexsum_bfloat16
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. -->
# bigbird_lora_multi_lexsum_bfloat16
This model is a fine-tuned version of [google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 9.2417
- Rouge1: 0.1892
- Rouge2: 0.0164
- Rougel: 0.1384
- Rougelsum: 0.1384
- Gen Len: 214.7444
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 9.2381 | 1.0 | 648 | 9.2448 | 0.1912 | 0.0175 | 0.1409 | 0.141 | 225.5222 |
| 9.1638 | 2.0 | 1296 | 9.2417 | 0.1892 | 0.0164 | 0.1384 | 0.1384 | 214.7444 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
Litzy619/V0410MP5 | Litzy619 | "2024-04-11T01:37:57Z" | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"region:us"
] | null | "2024-04-11T00:08:46Z" | ---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0410MP5
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. -->
# V0410MP5
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1568
## 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: 0.03
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 20
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2535 | 0.18 | 20 | 0.1664 |
| 0.1614 | 0.36 | 40 | 0.1584 |
| 0.1611 | 0.54 | 60 | 0.1564 |
| 0.1614 | 0.73 | 80 | 0.1567 |
| 0.1549 | 0.91 | 100 | 0.1555 |
| 0.1564 | 1.09 | 120 | 0.1574 |
| 0.1551 | 1.27 | 140 | 0.1557 |
| 0.156 | 1.45 | 160 | 0.1563 |
| 0.1571 | 1.63 | 180 | 0.1568 |
| 0.1533 | 1.81 | 200 | 0.1567 |
| 0.1584 | 1.99 | 220 | 0.1568 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Gusanidas/trilis_fln | Gusanidas | "2024-04-11T02:10:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | "2024-04-11T00:10:16Z" | Entry not found |
mscheny/mine3_18 | mscheny | "2024-06-03T07:14:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:10:23Z" | Entry not found |
mscheny/mine3_19 | mscheny | "2024-06-03T07:14:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:10:26Z" | Entry not found |
shtapm/whisper-large_0411_finetuning_decoder30_200steps | shtapm | "2024-04-11T00:13:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-04-11T00:10:40Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[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]
|
ahmetyaylalioglu/llama2_prompt_recover | ahmetyaylalioglu | "2024-04-11T00:13:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-2-13b-bnb-4bit",
"base_model:finetune:unsloth/llama-2-13b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-04-11T00:13:12Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-2-13b-bnb-4bit
---
# Uploaded model
- **Developed by:** ahmetyaylalioglu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-13b-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)
|
giantdev/m4h3 | giantdev | "2024-05-23T08:34:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:16:53Z" | Entry not found |
giantdev/m3h3 | giantdev | "2024-05-23T08:34:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:16:54Z" | Entry not found |
ALBADDAWI/DeepCode-7B-Aurora-v4 | ALBADDAWI | "2024-04-11T00:59:37Z" | 0 | 0 | null | [
"Kukedlc/NeuralMaths-Experiment-7b",
"lemon-mint/gemma-ko-7b-instruct-v0.62",
"Ppoyaa/StarMonarch-7B",
"automerger/YamshadowExperiment28-7B",
"ichigoberry/MonarchPipe-7B-slerp",
"deepseek-ai/deepseek-coder-7b-instruct-v1.5",
"Kukedlc/Neural-4-Maths-7b",
"base_model:Kukedlc/Neural-4-Maths-7b",
"base_model:finetune:Kukedlc/Neural-4-Maths-7b",
"region:us"
] | null | "2024-04-11T00:18:34Z" | ---
tags:
- Kukedlc/NeuralMaths-Experiment-7b
- lemon-mint/gemma-ko-7b-instruct-v0.62
- Ppoyaa/StarMonarch-7B
- automerger/YamshadowExperiment28-7B
- ichigoberry/MonarchPipe-7B-slerp
- deepseek-ai/deepseek-coder-7b-instruct-v1.5
- Kukedlc/Neural-4-Maths-7b
base_model:
- Kukedlc/NeuralMaths-Experiment-7b
- lemon-mint/gemma-ko-7b-instruct-v0.62
- Ppoyaa/StarMonarch-7B
- automerger/YamshadowExperiment28-7B
- ichigoberry/MonarchPipe-7B-slerp
- deepseek-ai/deepseek-coder-7b-instruct-v1.5
- Kukedlc/Neural-4-Maths-7b
---
# DeepCode-7B-Aurora-v4
DeepCode-7B-Aurora-v4 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b)
* [lemon-mint/gemma-ko-7b-instruct-v0.62](https://huggingface.co/lemon-mint/gemma-ko-7b-instruct-v0.62)
* [Ppoyaa/StarMonarch-7B](https://huggingface.co/Ppoyaa/StarMonarch-7B)
* [automerger/YamshadowExperiment28-7B](https://huggingface.co/automerger/YamshadowExperiment28-7B)
* [ichigoberry/MonarchPipe-7B-slerp](https://huggingface.co/ichigoberry/MonarchPipe-7B-slerp)
* [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5)
* [Kukedlc/Neural-4-Maths-7b](https://huggingface.co/Kukedlc/Neural-4-Maths-7b)
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuralMaths-Experiment-7b
parameters:
weight: 1
- model: lemon-mint/gemma-ko-7b-instruct-v0.62
parameters:
weight: 1
- model: Ppoyaa/StarMonarch-7B
parameters:
weight: 1
- model: automerger/YamshadowExperiment28-7B
parameters:
weight: 1
- model: ichigoberry/MonarchPipe-7B-slerp
parameters:
weight: 1
- model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
parameters:
weight: 1
- model: Kukedlc/Neural-4-Maths-7b
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai/deepseek-math-7b-rl
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v4"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v5 | ALBADDAWI | "2024-04-11T00:59:52Z" | 0 | 0 | null | [
"Kukedlc/NeuralMaths-Experiment-7b",
"lemon-mint/gemma-ko-7b-instruct-v0.62",
"Ppoyaa/StarMonarch-7B",
"automerger/YamshadowExperiment28-7B",
"ichigoberry/MonarchPipe-7B-slerp",
"deepseek-ai/deepseek-coder-7b-instruct-v1.5",
"Kukedlc/Neural-4-Maths-7b",
"base_model:Kukedlc/Neural-4-Maths-7b",
"base_model:finetune:Kukedlc/Neural-4-Maths-7b",
"region:us"
] | null | "2024-04-11T00:18:53Z" | ---
tags:
- Kukedlc/NeuralMaths-Experiment-7b
- lemon-mint/gemma-ko-7b-instruct-v0.62
- Ppoyaa/StarMonarch-7B
- automerger/YamshadowExperiment28-7B
- ichigoberry/MonarchPipe-7B-slerp
- deepseek-ai/deepseek-coder-7b-instruct-v1.5
- Kukedlc/Neural-4-Maths-7b
base_model:
- Kukedlc/NeuralMaths-Experiment-7b
- lemon-mint/gemma-ko-7b-instruct-v0.62
- Ppoyaa/StarMonarch-7B
- automerger/YamshadowExperiment28-7B
- ichigoberry/MonarchPipe-7B-slerp
- deepseek-ai/deepseek-coder-7b-instruct-v1.5
- Kukedlc/Neural-4-Maths-7b
---
# DeepCode-7B-Aurora-v5
DeepCode-7B-Aurora-v5 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuralMaths-Experiment-7b](https://huggingface.co/Kukedlc/NeuralMaths-Experiment-7b)
* [lemon-mint/gemma-ko-7b-instruct-v0.62](https://huggingface.co/lemon-mint/gemma-ko-7b-instruct-v0.62)
* [Ppoyaa/StarMonarch-7B](https://huggingface.co/Ppoyaa/StarMonarch-7B)
* [automerger/YamshadowExperiment28-7B](https://huggingface.co/automerger/YamshadowExperiment28-7B)
* [ichigoberry/MonarchPipe-7B-slerp](https://huggingface.co/ichigoberry/MonarchPipe-7B-slerp)
* [deepseek-ai/deepseek-coder-7b-instruct-v1.5](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5)
* [Kukedlc/Neural-4-Maths-7b](https://huggingface.co/Kukedlc/Neural-4-Maths-7b)
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuralMaths-Experiment-7b
parameters:
weight: 1
- model: lemon-mint/gemma-ko-7b-instruct-v0.62
parameters:
weight: 1
- model: Ppoyaa/StarMonarch-7B
parameters:
weight: 1
- model: automerger/YamshadowExperiment28-7B
parameters:
weight: 1
- model: ichigoberry/MonarchPipe-7B-slerp
parameters:
weight: 1
- model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
parameters:
weight: 1
- model: Kukedlc/Neural-4-Maths-7b
parameters:
weight: 1
merge_method: model_stock
base_model: deepseek-ai/deepseek-math-7b-rl
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v5"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
GSBoom/whakai | GSBoom | "2024-04-11T00:19:02Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-04-11T00:19:01Z" | ---
license: mit
---
|
ALBADDAWI/DeepCode-7B-Aurora-v6 | ALBADDAWI | "2024-04-11T01:00:12Z" | 0 | 0 | null | [
"DeepCode-7B-Aurora-v4",
"region:us"
] | null | "2024-04-11T00:19:09Z" | ---
tags:
- DeepCode-7B-Aurora-v4
base_model:
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
---
# DeepCode-7B-Aurora-v6
DeepCode-7B-Aurora-v6 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
merge_method: task_arithmetic
base_model: deepseek-ai/deepseek-math-7b-rl
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v6"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v7 | ALBADDAWI | "2024-04-11T01:00:26Z" | 0 | 0 | null | [
"DeepCode-7B-Aurora-v4",
"region:us"
] | null | "2024-04-11T00:19:24Z" | ---
tags:
- DeepCode-7B-Aurora-v4
base_model:
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v4
---
# DeepCode-7B-Aurora-v7
DeepCode-7B-Aurora-v7 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
merge_method: task_arithmetic
base_model: DeepCode-7B-Aurora-v4
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v7"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v8 | ALBADDAWI | "2024-04-11T01:00:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:19:40Z" | ---
{}
---
# DeepCode-7B-Aurora-v8
DeepCode-7B-Aurora-v8 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v4
merge_method: model_stock
base_model: DeepCode-7B-Aurora-v4
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v8"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v9 | ALBADDAWI | "2024-04-11T01:00:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:19:58Z" | ---
{}
---
# DeepCode-7B-Aurora-v9
DeepCode-7B-Aurora-v9 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v4
- model: DeepCode-7B-Aurora-v5
- model: DeepCode-7B-Aurora-v6
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v6
- model: DeepCode-7B-Aurora-v5
merge_method: model_stock
base_model: DeepCode-7B-Aurora-v4
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v9"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v10 | ALBADDAWI | "2024-04-11T01:01:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:20:13Z" | ---
{}
---
# DeepCode-7B-Aurora-v10
DeepCode-7B-Aurora-v10 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
- model: DeepCode-7B-Aurora-v7
merge_method: model_stock
base_model: DeepCode-7B-Aurora-v7
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v10"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
ALBADDAWI/DeepCode-7B-Aurora-v11 | ALBADDAWI | "2024-04-11T01:01:26Z" | 0 | 0 | null | [
"DeepCode-7B-Aurora-v4",
"DeepCode-7B-Aurora-v5",
"DeepCode-7B-Aurora-v6",
"DeepCode-7B-Aurora-v7",
"DeepCode-7B-Aurora-v8",
"DeepCode-7B-Aurora-v9",
"DeepCode-7B-Aurora-v10",
"region:us"
] | null | "2024-04-11T00:20:28Z" | ---
tags:
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v5
- DeepCode-7B-Aurora-v6
- DeepCode-7B-Aurora-v7
- DeepCode-7B-Aurora-v8
- DeepCode-7B-Aurora-v9
- DeepCode-7B-Aurora-v10
base_model:
- DeepCode-7B-Aurora-v4
- DeepCode-7B-Aurora-v5
- DeepCode-7B-Aurora-v6
- DeepCode-7B-Aurora-v7
- DeepCode-7B-Aurora-v8
- DeepCode-7B-Aurora-v9
- DeepCode-7B-Aurora-v10
---
# DeepCode-7B-Aurora-v11
DeepCode-7B-Aurora-v11 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
* [DeepCode-7B-Aurora-v5](https://huggingface.co/DeepCode-7B-Aurora-v5)
* [DeepCode-7B-Aurora-v6](https://huggingface.co/DeepCode-7B-Aurora-v6)
* [DeepCode-7B-Aurora-v7](https://huggingface.co/DeepCode-7B-Aurora-v7)
* [DeepCode-7B-Aurora-v8](https://huggingface.co/DeepCode-7B-Aurora-v8)
* [DeepCode-7B-Aurora-v9](https://huggingface.co/DeepCode-7B-Aurora-v9)
* [DeepCode-7B-Aurora-v10](https://huggingface.co/DeepCode-7B-Aurora-v10)
## 🧩 Configuration
```yaml
models:
- model: DeepCode-7B-Aurora-v4
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v5
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v6
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v7
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v8
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v9
parameters:
weight: 1
- model: DeepCode-7B-Aurora-v10
parameters:
weight: 1
merge_method: task_arithmetic
base_model: DeepCode-7B-Aurora-v7
parameters:
normalize: true
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ALBADDAWI/DeepCode-7B-Aurora-v11"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
AISeneca/MIxtralino | AISeneca | "2024-04-11T00:21:38Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:21:37Z" | Entry not found |
oneandahalfcats/2202 | oneandahalfcats | "2024-04-11T00:22:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:22:51Z" | Entry not found |
NavaneethNivol/ResuLlama-2-ai-hf | NavaneethNivol | "2024-04-11T00:30:50Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2024-04-11T00:30:28Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
IamYash/VA-LLM-x9lp03uo | IamYash | "2024-04-11T04:41:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:32:21Z" | Entry not found |
pminervini/Llama-2-7b-hf_bs_1_lr_3e-05_lorarank_64 | pminervini | "2024-04-11T01:52:25Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-04-11T00:35:59Z" | Entry not found |
basurasensual05/rostro | basurasensual05 | "2024-04-11T00:49:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:47:45Z" | Entry not found |
kitagawape/Models | kitagawape | "2024-04-11T01:09:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:50:08Z" | Entry not found |
Vinnyyw/Anysolos | Vinnyyw | "2024-04-11T00:52:12Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-04-11T00:50:48Z" | ---
license: openrail
---
|
ledmands/dqn_Pacman-v5_batch64_v2 | ledmands | "2024-04-11T00:53:10Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"ALE/Pacman-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-04-11T00:52:41Z" | ---
library_name: stable-baselines3
tags:
- ALE/Pacman-v5
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/Pacman-v5
type: ALE/Pacman-v5
metrics:
- type: mean_reward
value: 245.80 +/- 163.33
name: mean_reward
verified: false
---
# **DQN** Agent playing **ALE/Pacman-v5**
This is a trained model of a **DQN** agent playing **ALE/Pacman-v5**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Pacman-v5 -orga ledmands -f logs/
python -m rl_zoo3.enjoy --algo dqn --env ALE/Pacman-v5 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env ALE/Pacman-v5 -orga ledmands -f logs/
python -m rl_zoo3.enjoy --algo dqn --env ALE/Pacman-v5 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env ALE/Pacman-v5 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env ALE/Pacman-v5 -f logs/ -orga ledmands
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 500000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Litzy619/V0410MP6 | Litzy619 | "2024-04-11T02:29:57Z" | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"region:us"
] | null | "2024-04-11T00:58:35Z" | ---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0410MP6
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. -->
# V0410MP6
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1573
## 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: 0.03
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 20
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2535 | 0.18 | 20 | 0.1664 |
| 0.1614 | 0.36 | 40 | 0.1584 |
| 0.1611 | 0.54 | 60 | 0.1564 |
| 0.1614 | 0.73 | 80 | 0.1567 |
| 0.1549 | 0.91 | 100 | 0.1555 |
| 0.1565 | 1.09 | 120 | 0.1573 |
| 0.1553 | 1.27 | 140 | 0.1578 |
| 0.1554 | 1.45 | 160 | 0.1564 |
| 0.1572 | 1.63 | 180 | 0.1578 |
| 0.1534 | 1.81 | 200 | 0.1573 |
| 0.1581 | 1.99 | 220 | 0.1573 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
camenduru/tgi | camenduru | "2024-04-11T01:04:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T00:59:29Z" | Entry not found |
VoidGivenForm/lora | VoidGivenForm | "2024-04-11T02:52:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T01:00:20Z" | Entry not found |
elonniu/esd | elonniu | "2024-07-16T04:14:19Z" | 0 | 0 | null | [
"onnx",
"region:us"
] | null | "2024-04-11T01:01:21Z" | Entry not found |
neotran/gemma-1.1-2b-it-med-qa | neotran | "2024-04-11T04:16:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-11T01:02:52Z" | ---
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]
|
oneandahalfcats/31583 | oneandahalfcats | "2024-04-11T01:03:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-04-11T01:03:42Z" | Entry not found |
sunnythakkar/refl | sunnythakkar | "2024-04-11T05:56:05Z" | 0 | 0 | diffusers | [
"diffusers",
"diffusers:UNet2DConditionModel",
"region:us"
] | null | "2024-04-11T01:04:07Z" | Entry not found |
byeolcardi/kojp_translator | byeolcardi | "2024-04-11T04:30:06Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-04-11T01:04:35Z" | ---
library_name: peft
base_model: google/gemma-2b
---
|
potradovec/gpt2-reuters-tokenizer | potradovec | "2024-04-11T01:05:04Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-11T01:05:03Z" | ---
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]
|
venkateshmurugadas/dophin-gemma-2b-sft-dolly-chatml-adapter | venkateshmurugadas | "2024-04-11T03:49:57Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:cognitivecomputations/dolphin-2.8-gemma-2b",
"base_model:adapter:cognitivecomputations/dolphin-2.8-gemma-2b",
"region:us"
] | null | "2024-04-11T01:06:30Z" | ---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: cognitivecomputations/dolphin-2.8-gemma-2b
model-index:
- name: dophin-gemma-2b-sft-dolly-chatml-adapter
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. -->
# dophin-gemma-2b-sft-dolly-chatml-adapter
This model is a fine-tuned version of [cognitivecomputations/dolphin-2.8-gemma-2b](https://huggingface.co/cognitivecomputations/dolphin-2.8-gemma-2b) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 |
NavaneethNivol/ResuLlama-2-data-hf | NavaneethNivol | "2024-04-11T01:09:08Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2024-04-11T01:08:43Z" | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|