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2025-06-28 00:40:13
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ywiyogo/q-Taxi-v3 | ywiyogo | 2025-05-03T07:52:51Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-03T07:52:49Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
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="ywiyogo/q-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"])
```
|
grapevine-AI/Qwen3-30B-A3B-GGUF | grapevine-AI | 2025-05-03T07:46:48Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T07:14:33Z | ---
license: apache-2.0
---
# What is this?
Alibaba Cloudの思考/非思考ハイブリッドMoEモデル、[Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)を日本語imatrixで量子化したものです。
# imatrix dataset
日本語能力を重視し、日本語が多量に含まれる[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)データセットを使用しました。
# Chat template
```
<|im_start|>system
ここにSystem Promptを書きます。<|im_end|>
<|im_start|>user
ここにMessageを書きます。<|im_end|>
<|im_start|>assistant
```
# Quants
各クオンツとそのベンチマークスコア（Gemini 2.0 Flash採点によるElyza_tasks 100）をまとめておきます。
- 思考あり
|クオンツ|スコア|コメント|
|---|---|---|
|Q8_0|4.41||
|Q6_K|4.44||
|Q5_K_M|4.46|推奨|
|Q4_K_M|4.44||
|IQ4_XS|4.43||
- 思考なし
|クオンツ|スコア|コメント|
|---|---|---|
|Q8_0|4.06||
|Q6_K|4.09||
|Q5_K_M|4.18|推奨|
|Q4_K_M|4.07||
|IQ4_XS|3.98||
# Environment
Windows版llama.cpp-b5218および同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。
# License
Apache 2.0
# Developer
Alibaba Cloud |
sommerzen/qwemani-3-4b_v2 | sommerzen | 2025-05-03T07:46:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T07:40:25Z | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sommerzen
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 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)
|
mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF | mradermacher | 2025-05-03T07:44:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564",
"base_model:quantized:fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564",
"endpoints_compatible",
"region:us",
"imatrix",
"feature-extraction"
] | null | 2025-05-03T07:41:41Z | ---
base_model: fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-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/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ1_M.gguf) | i1-IQ1_M | 0.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ2_S.gguf) | i1-IQ2_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ2_M.gguf) | i1-IQ2_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q2_K.gguf) | i1-Q2_K | 0.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ3_S.gguf) | i1-IQ3_S | 0.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ3_M.gguf) | i1-IQ3_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q4_0.gguf) | i1-Q4_0 | 0.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q4_1.gguf) | i1-Q4_1 | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-i1-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.i1-Q6_K.gguf) | i1-Q6_K | 0.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF | mradermacher | 2025-05-03T07:43:39Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564",
"base_model:quantized:fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-05-03T07:41:17Z | ---
base_model: fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/fine-tuned/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-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/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q5_K_S.gguf) | Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q5_K_M.gguf) | Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564-GGUF/resolve/main/medical-100-64-16-jinaai_jina-embeddings-v2-small-en-100-gpt-3.5-turbo_9062874564.f16.gguf) | f16 | 0.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
fasasimounpedsf/SDVDFVB | fasasimounpedsf | 2025-05-03T07:43:36Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-03T07:43:36Z | ---
license: bigscience-openrail-m
---
|
mradermacher/II-Medical-7B-Preview-i1-GGUF | mradermacher | 2025-05-03T07:39:58Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Intelligent-Internet/II-Medical-7B-Preview",
"base_model:quantized:Intelligent-Internet/II-Medical-7B-Preview",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T00:05:03Z | ---
base_model: Intelligent-Internet/II-Medical-7B-Preview
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Intelligent-Internet/II-Medical-7B-Preview
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/II-Medical-7B-Preview-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/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/II-Medical-7B-Preview-i1-GGUF/resolve/main/II-Medical-7B-Preview.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ponytail/Face-LLaVA_Qwen2.5-3B | ponytail | 2025-05-03T07:38:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"AIGC",
"LLaVA",
"visual-question-answering",
"dataset:OpenFace-CQUPT/FaceCaption-15M",
"arxiv:2411.03034",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"endpoints_compatible",
"region:us"
] | visual-question-answering | 2025-05-03T05:02:30Z | ---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
- AIGC
- LLaVA
datasets:
- OpenFace-CQUPT/FaceCaption-15M
metrics:
- accuracy
pipeline_tag: visual-question-answering
---
# Human-LLaVA-8B
## DEMO
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64259db7d3e6fdf87e4792d0/TpN2t19Poe5YbHHP8uN7_.mp4"></video>
### Introduction
Human-related vision and language tasks are widely applied across various social scenarios. The latest studies demonstrate that the large vision-language model can enhance the performance of various downstream tasks in visual-language understanding. Since, models in the general domain often not perform well in the specialized field. In this study, we train a domain-specific Large Language-Vision model, Human-LLaVA, which aim to construct an unified multimodal Language-Vision Model for Human-related tasks.
Specifically, (1) we first construct **a large-scale and high-quality human-related image-text (caption) dataset** extracted from Internet for domain-specific alignment in the first stage (Coming soon); (2) we also propose to construct **a multi-granularity caption for human-related images** (Coming soon), including human face, human body, and whole image, thereby fine-tuning a large language model. Lastly, we evaluate our model on a series of downstream tasks, our **Human-LLaVA** achieved the best overall performance among multimodal models of similar scale. In particular, it exhibits the best performance in a series of human-related tasks, significantly surpassing similar models and ChatGPT-4o. We believe that the Huaman-LLaVA model and a series of datasets presented in this work can promote research in related fields.
## Result
human-llava has a good performance in both general and special fields
## News and Update 🔥🔥🔥
* Oct.23, 2024. **🤗[HumanCaption-HQ-311K](https://huggingface.co/datasets/OpenFace-CQUPT/HumanCaption-HQ-311K), is released!👏👏👏**
* Sep.12, 2024. **🤗[HumanCaption-10M](https://huggingface.co/datasets/OpenFace-CQUPT/HumanCaption-10M), is released!👏👏👏**
* Sep.8, 2024. **🤗[HumanVLM](https://huggingface.co/OpenFace-CQUPT/Human_LLaVA), is released!👏👏👏**
## 🤗 Transformers
To use Human-LLaVA for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, please make sure that you are using latest code.
``` python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, AutoModelForPreTraining
model_id = "OpenFace-CQUPT/Human_LLaVA"
cuda = 0
model = AutoModelForPreTraining.from_pretrained("OpenFace-CQUPT/Human_LLaVA", torch_dtype=torch.float16).to(cuda)
processor = AutoProcessor.from_pretrained(model_id,trust_remote_code=True)
text = "Please describe this picture"
prompt = "USER: <image>\n" + text + "\nASSISTANT:"
image_file = "./test1.jpg"
raw_image = Image.open(image_file)
# raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(cuda, torch.float16)
output = model.generate(**inputs, max_new_tokens=400, do_sample=False)
predict = processor.decode(output[0][:], skip_special_tokens=True)
print(predict)
```
Our training code have been released publicly on github.[ddw2AIGROUP2CQUPT/Human-LLaVA-8B(github.com)](https://github.com/ddw2AIGROUP2CQUPT/Human-LLaVA-8B)
## Get the Dataset
#### Dataset Example
#### Domain Alignment Stage
[HumanCaption-10M](https://huggingface.co/datasets/OpenFace-CQUPT/HumanCaption-10M)(self construct): is released!
#### Instruction Tuning Stage
**All public data sets have been filtered, and we will consider publishing all processed text in the future**
[HumanCaption-HQ](https://huggingface.co/datasets/OpenFace-CQUPT/HumanCaption-HQ-311K)(self construct): is released!
[FaceCaptionA](https://huggingface.co/datasets/OpenFace-CQUPT/FaceCaption-15M)(self construct): is released!
CelebA: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
ShareGPT4V:https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md
LLaVA-Instruct_zh : https://huggingface.co/datasets/openbmb/llava_zh
verified_ref3rec: https://huggingface.co/datasets/lucasjin/refcoco/blob/main/ref3rec.json
verified_ref3reg: https://huggingface.co/datasets/lucasjin/refcoco/blob/main/ref3rec.json
verified_shikra: https://github.com/shikras/shikra
## Citation
```
@misc{dai2024humanvlmfoundationhumanscenevisionlanguage,
title={HumanVLM: Foundation for Human-Scene Vision-Language Model},
author={Dawei Dai and Xu Long and Li Yutang and Zhang Yuanhui and Shuyin Xia},
year={2024},
eprint={2411.03034},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2411.03034},
}
```
## contact
mailto: [[email protected]](mailto:[email protected]) or [[email protected]](mailto:[email protected]) |
bartalex31/mlmodel | bartalex31 | 2025-05-03T07:33:19Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T07:33:19Z | ---
license: apache-2.0
---
|
Banki42/model | Banki42 | 2025-05-03T07:20:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T07:19:28Z | ---
base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Banki42
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit
This qwen3 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)
|
Hachipo/Qwen2.5-7B-MIFT-en_10000_2 | Hachipo | 2025-05-03T07:16:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T07:12:49Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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] |
gtkunit/Qwen3-235B-A22B-2.0bpw-h6-exl2 | gtkunit | 2025-05-03T07:14:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T07:14:44Z | ---
license: apache-2.0
---
|
jahyungu/Llama-3.2-1B-Instruct_MetaMathQA-40K_cluster9 | jahyungu | 2025-05-03T07:14:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T05:31:32Z | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct_MetaMathQA-40K_cluster9
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. -->
# Llama-3.2-1B-Instruct_MetaMathQA-40K_cluster9
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
isbistloui/math-llama-aiml428-a2 | isbistloui | 2025-05-03T07:06:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T07:06:28Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF | mradermacher | 2025-05-03T07:06:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Mumamonster/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical",
"base_model:quantized:Mumamonster/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T06:34:58Z | ---
base_model: Mumamonster/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Mumamonster/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-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/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ1_S.gguf) | i1-IQ1_S | 0.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ2_S.gguf) | i1-IQ2_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ2_M.gguf) | i1-IQ2_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q2_K.gguf) | i1-Q2_K | 0.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.9 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ3_S.gguf) | i1-IQ3_S | 0.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ3_M.gguf) | i1-IQ3_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical-i1-GGUF/resolve/main/Qwen2.5-1.5B-Instruct-Distill_all_putonghua_medical.i1-Q6_K.gguf) | i1-Q6_K | 1.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
bartowski/kalomaze_Qwen3-16B-A3B-GGUF | bartowski | 2025-05-03T06:48:46Z | 0 | 2 | null | [
"gguf",
"text-generation",
"base_model:kalomaze/Qwen3-16B-A3B",
"base_model:quantized:kalomaze/Qwen3-16B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-03T05:14:31Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
license: apache-2.0
base_model_relation: quantized
base_model: kalomaze/Qwen3-16B-A3B
---
## Llamacpp imatrix Quantizations of Qwen3-16B-A3B by kalomaze
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5255">b5255</a> for quantization.
Original model: https://huggingface.co/kalomaze/Qwen3-16B-A3B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Qwen3-16B-A3B-bf16.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-bf16.gguf) | bf16 | 32.08GB | false | Full BF16 weights. |
| [Qwen3-16B-A3B-Q8_0.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q8_0.gguf) | Q8_0 | 17.06GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Qwen3-16B-A3B-Q6_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q6_K_L.gguf) | Q6_K_L | 13.34GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Qwen3-16B-A3B-Q6_K.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q6_K.gguf) | Q6_K | 13.19GB | false | Very high quality, near perfect, *recommended*. |
| [Qwen3-16B-A3B-Q5_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_L.gguf) | Q5_K_L | 11.62GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Qwen3-16B-A3B-Q5_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_M.gguf) | Q5_K_M | 11.43GB | false | High quality, *recommended*. |
| [Qwen3-16B-A3B-Q5_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q5_K_S.gguf) | Q5_K_S | 11.11GB | false | High quality, *recommended*. |
| [Qwen3-16B-A3B-Q4_1.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_1.gguf) | Q4_1 | 10.13GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Qwen3-16B-A3B-Q4_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_L.gguf) | Q4_K_L | 10.06GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Qwen3-16B-A3B-Q4_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf) | Q4_K_M | 9.83GB | false | Good quality, default size for most use cases, *recommended*. |
| [Qwen3-16B-A3B-Q4_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_K_S.gguf) | Q4_K_S | 9.50GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Qwen3-16B-A3B-Q4_0.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q4_0.gguf) | Q4_0 | 9.30GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Qwen3-16B-A3B-IQ4_NL.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ4_NL.gguf) | IQ4_NL | 9.21GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Qwen3-16B-A3B-IQ4_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ4_XS.gguf) | IQ4_XS | 8.73GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Qwen3-16B-A3B-Q3_K_XL.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_XL.gguf) | Q3_K_XL | 7.98GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Qwen3-16B-A3B-Q3_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_L.gguf) | Q3_K_L | 7.71GB | false | Lower quality but usable, good for low RAM availability. |
| [Qwen3-16B-A3B-Q3_K_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_M.gguf) | Q3_K_M | 7.50GB | false | Low quality. |
| [Qwen3-16B-A3B-IQ3_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_M.gguf) | IQ3_M | 7.50GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Qwen3-16B-A3B-Q3_K_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q3_K_S.gguf) | Q3_K_S | 7.17GB | false | Low quality, not recommended. |
| [Qwen3-16B-A3B-IQ3_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_XS.gguf) | IQ3_XS | 6.82GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Qwen3-16B-A3B-IQ3_XXS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ3_XXS.gguf) | IQ3_XXS | 6.53GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Qwen3-16B-A3B-Q2_K_L.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q2_K_L.gguf) | Q2_K_L | 6.19GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Qwen3-16B-A3B-Q2_K.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-Q2_K.gguf) | Q2_K | 5.88GB | false | Very low quality but surprisingly usable. |
| [Qwen3-16B-A3B-IQ2_M.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_M.gguf) | IQ2_M | 5.62GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Qwen3-16B-A3B-IQ2_S.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_S.gguf) | IQ2_S | 5.01GB | false | Low quality, uses SOTA techniques to be usable. |
| [Qwen3-16B-A3B-IQ2_XS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_XS.gguf) | IQ2_XS | 4.93GB | false | Low quality, uses SOTA techniques to be usable. |
| [Qwen3-16B-A3B-IQ2_XXS.gguf](https://huggingface.co/bartowski/kalomaze_Qwen3-16B-A3B-GGUF/blob/main/kalomaze_Qwen3-16B-A3B-IQ2_XXS.gguf) | IQ2_XXS | 4.43GB | false | Very low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
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/kalomaze_Qwen3-16B-A3B-GGUF --include "kalomaze_Qwen3-16B-A3B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/kalomaze_Qwen3-16B-A3B-GGUF --include "kalomaze_Qwen3-16B-A3B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (kalomaze_Qwen3-16B-A3B-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
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, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
prithivMLmods/Omni-Reasoner-2B | prithivMLmods | 2025-05-03T06:47:22Z | 9 | 4 | transformers | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"text-generation-inference",
"Omni",
"Math",
"Reasoner",
"Qwen-Base",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-01-16T22:46:47Z | ---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2-VL-2B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- Omni
- Math
- Reasoner
- Qwen-Base
---
# **Omni-Reasoner-2B [VL/ Doc OCR]**
*Omni-Reasoner-2B* is based on Qwen2VL and is designed for mathematical and content-based explanations. It excels in providing detailed reasoning about content and solving math problems with proper content formatting. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
# **Use it with Transformers**
*Before using, ensure that the required libraries are successfully installed in the environment.*
!pip install gradio spaces transformers accelerate numpy requests torch torchvision qwen-vl-utils av ipython reportlab fpdf python-docx pillow huggingface_hub
*ChemQwen With Inference Documentation, **Before using, make sure that the `hf_token` is provided in the login field in the code below.***
# **Sample Inference with Doc**
📒*Demo:* https://huggingface.co/prithivMLmods/Omni-Reasoner-2B/blob/main/Omni-R/omni-r.ipynb
```python
# Authenticate with Hugging Face
from huggingface_hub import login
# Log in to Hugging Face using the provided token
hf_token = '----xxxxx----'
login(hf_token)
# Demo
import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer
from qwen_vl_utils import process_vision_info
import torch
from PIL import Image
import os
import uuid
import io
from threading import Thread
from reportlab.lib.pagesizes import A4
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.platypus import SimpleDocTemplate, Image as RLImage, Paragraph, Spacer
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont
import docx
from docx.enum.text import WD_ALIGN_PARAGRAPH
# Define model options
MODEL_OPTIONS = {
"Omni-Reasoner": "prithivMLmods/Omni-Reasoner-2B",
}
# Preload models and processors into CUDA
models = {}
processors = {}
for name, model_id in MODEL_OPTIONS.items():
print(f"Loading {name}...")
models[name] = Qwen2VLForConditionalGeneration.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype=torch.float16
).to("cuda").eval()
processors[name] = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
image_extensions = Image.registered_extensions()
def identify_and_save_blob(blob_path):
"""Identifies if the blob is an image and saves it."""
try:
with open(blob_path, 'rb') as file:
blob_content = file.read()
try:
Image.open(io.BytesIO(blob_content)).verify() # Check if it's a valid image
extension = ".png" # Default to PNG for saving
media_type = "image"
except (IOError, SyntaxError):
raise ValueError("Unsupported media type. Please upload a valid image.")
filename = f"temp_{uuid.uuid4()}_media{extension}"
with open(filename, "wb") as f:
f.write(blob_content)
return filename, media_type
except FileNotFoundError:
raise ValueError(f"The file {blob_path} was not found.")
except Exception as e:
raise ValueError(f"An error occurred while processing the file: {e}")
@spaces.GPU
def qwen_inference(model_name, media_input, text_input=None):
"""Handles inference for the selected model."""
model = models[model_name]
processor = processors[model_name]
if isinstance(media_input, str):
media_path = media_input
if media_path.endswith(tuple([i for i in image_extensions.keys()])):
media_type = "image"
else:
try:
media_path, media_type = identify_and_save_blob(media_input)
except Exception as e:
raise ValueError("Unsupported media type. Please upload a valid image.")
messages = [
{
"role": "user",
"content": [
{
"type": media_type,
media_type: media_path
},
{"type": "text", "text": text_input},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, _ = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
streamer = TextIteratorStreamer(
processor.tokenizer, skip_prompt=True, skip_special_tokens=True
)
generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=1024)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
# Remove <|im_end|> or similar tokens from the output
buffer = buffer.replace("<|im_end|>", "")
yield buffer
def format_plain_text(output_text):
"""Formats the output text as plain text without LaTeX delimiters."""
# Remove LaTeX delimiters and convert to plain text
plain_text = output_text.replace("\\(", "").replace("\\)", "").replace("\\[", "").replace("\\]", "")
return plain_text
def generate_document(media_path, output_text, file_format, font_size, line_spacing, alignment, image_size):
"""Generates a document with the input image and plain text output."""
plain_text = format_plain_text(output_text)
if file_format == "pdf":
return generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size)
elif file_format == "docx":
return generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size)
def generate_pdf(media_path, plain_text, font_size, line_spacing, alignment, image_size):
"""Generates a PDF document."""
filename = f"output_{uuid.uuid4()}.pdf"
doc = SimpleDocTemplate(
filename,
pagesize=A4,
rightMargin=inch,
leftMargin=inch,
topMargin=inch,
bottomMargin=inch
)
styles = getSampleStyleSheet()
styles["Normal"].fontSize = int(font_size)
styles["Normal"].leading = int(font_size) * line_spacing
styles["Normal"].alignment = {
"Left": 0,
"Center": 1,
"Right": 2,
"Justified": 4
}[alignment]
story = []
# Add image with size adjustment
image_sizes = {
"Small": (200, 200),
"Medium": (400, 400),
"Large": (600, 600)
}
img = RLImage(media_path, width=image_sizes[image_size][0], height=image_sizes[image_size][1])
story.append(img)
story.append(Spacer(1, 12))
# Add plain text output
text = Paragraph(plain_text, styles["Normal"])
story.append(text)
doc.build(story)
return filename
def generate_docx(media_path, plain_text, font_size, line_spacing, alignment, image_size):
"""Generates a DOCX document."""
filename = f"output_{uuid.uuid4()}.docx"
doc = docx.Document()
# Add image with size adjustment
image_sizes = {
"Small": docx.shared.Inches(2),
"Medium": docx.shared.Inches(4),
"Large": docx.shared.Inches(6)
}
doc.add_picture(media_path, width=image_sizes[image_size])
doc.add_paragraph()
# Add plain text output
paragraph = doc.add_paragraph()
paragraph.paragraph_format.line_spacing = line_spacing
paragraph.paragraph_format.alignment = {
"Left": WD_ALIGN_PARAGRAPH.LEFT,
"Center": WD_ALIGN_PARAGRAPH.CENTER,
"Right": WD_ALIGN_PARAGRAPH.RIGHT,
"Justified": WD_ALIGN_PARAGRAPH.JUSTIFY
}[alignment]
run = paragraph.add_run(plain_text)
run.font.size = docx.shared.Pt(int(font_size))
doc.save(filename)
return filename
# CSS for output styling
css = """
#output {
height: 500px;
overflow: auto;
border: 1px solid #ccc;
}
.submit-btn {
background-color: #cf3434 !important;
color: white !important;
}
.submit-btn:hover {
background-color: #ff2323 !important;
}
.download-btn {
background-color: #35a6d6 !important;
color: white !important;
}
.download-btn:hover {
background-color: #22bcff !important;
}
"""
# Gradio app setup
with gr.Blocks(css=css) as demo:
gr.Markdown("# ChemQwen Chemical Identifier")
with gr.Tab(label="Image Input"):
with gr.Row():
with gr.Column():
model_choice = gr.Dropdown(
label="Model Selection",
choices=list(MODEL_OPTIONS.keys()),
value="Omni-Reasoner"
)
input_media = gr.File(
label="Upload Image", type="filepath"
)
text_input = gr.Textbox(label="Question", placeholder="Ask a question about the image...")
submit_btn = gr.Button(value="Submit", elem_classes="submit-btn")
with gr.Column():
output_text = gr.Textbox(label="Output Text", lines=10)
plain_text_output = gr.Textbox(label="Standardized Plain Text", lines=10)
submit_btn.click(
qwen_inference, [model_choice, input_media, text_input], [output_text]
).then(
lambda output_text: format_plain_text(output_text), [output_text], [plain_text_output]
)
# Add examples directly usable by clicking
with gr.Row():
with gr.Column():
line_spacing = gr.Dropdown(
choices=[0.5, 1.0, 1.15, 1.5, 2.0, 2.5, 3.0],
value=1.5,
label="Line Spacing"
)
font_size = gr.Dropdown(
choices=["8", "10", "12", "14", "16", "18", "20", "22", "24"],
value="18",
label="Font Size"
)
alignment = gr.Dropdown(
choices=["Left", "Center", "Right", "Justified"],
value="Justified",
label="Text Alignment"
)
image_size = gr.Dropdown(
choices=["Small", "Medium", "Large"],
value="Small",
label="Image Size"
)
file_format = gr.Radio(["pdf", "docx"], label="File Format", value="pdf")
get_document_btn = gr.Button(value="Get Document", elem_classes="download-btn")
get_document_btn.click(
generate_document, [input_media, output_text, file_format, font_size, line_spacing, alignment, image_size], gr.File(label="Download Document")
)
demo.launch(debug=True)
```
# **Key Enhancements**
1. **Advanced Reasoning Capabilities**:
- Enhanced ability to perform long-form reasoning for complex mathematical and content-based queries.
- Supports detailed step-by-step explanations for problem-solving and content formatting.
2. **Multi-Modal Integration**:
- Combines visual and textual understanding to interpret and analyze diverse input formats (images, text, and mathematical expressions).
3. **Conversational Workflow**:
- Offers a natural conversational interface for interactive problem-solving and explanations.
4. **Content Formatting**:
- Improves content presentation with structured formatting for better readability and understanding.
# **Intended Use**
1. **Educational Assistance**:
- Ideal for students and educators for solving mathematical problems, creating structured explanations, and formatting educational content.
2. **Research Support**:
- Assists researchers in generating in-depth explanations and interpreting complex visual and textual data.
3. **Content Creation**:
- Enhances the generation of well-formatted documents, reports, and presentations.
4. **General Purpose Assistance**:
- Useful for applications requiring long-form reasoning and conversational AI in domains like tutoring, customer support, and technical writing.
# **Limitations**
1. **Domain-Specific Expertise**:
- May struggle with niche or highly specialized topics outside its training domain.
2. **Error in Long-Chain Reasoning**:
- In rare cases, it might generate incorrect or inconsistent solutions for highly complex problems.
3. **Visual Data Limitations**:
- Performance may depend on the quality and clarity of visual inputs (e.g., low-resolution images may reduce accuracy).
4. **Formatting Constraints**:
- While effective, complex or heavily customized formatting tasks may require manual adjustments.
5. **Dependence on Context**:
- The model relies on well-structured input to produce accurate and coherent outputs; ambiguous or incomplete prompts may lead to suboptimal results. |
OpenMOSE/PRWKV-7-Qwen3-Preview-v0.1 | OpenMOSE | 2025-05-03T06:44:16Z | 0 | 1 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-30T19:11:42Z | ---
license: apache-2.0
---
# **Model Card: PRWKV-7-Qwen3-14B-Preview-v0.1**
### **Overview**
- **Model Name:** PRWKV-7-Qwen3-14B-Preview-v0.1
- **Base Model:** Qwen3 14B (Instruct)
- **Architecture:** RWKV Cxa076r (RWKV x070 Based) + SwiGLU
- **Parameter Count:** 14 Billion
- **Context Length:** 3072
- **Training Tokens:**
- Stage 1: 100 Million Tokens
- Stage 2: 200 Million Tokens
This model is part of an experimental effort to *replace Transformer-style attention with a fully recurrent RWKV-based architecture*. It uses a customized version of the RWKV TimeMix block (`Cxa076r`) with SwiGLU activation, applied to a 14B-scale model derived from Qwen3.
---
### **Motivation**
The goal of this project is to explore whether an RNN-style model such as RWKV can faithfully mimic the output and reasoning behavior of large Transformer-based LLMs like Qwen3, while retaining the benefits of linear compute cost and persistent memory.
Replacing attention with TimeMix was not a trivial task. Qwen3 is heavily optimized for attention-based flow, including grouped-query attention (GQA) and Rotary Positional Embeddings (RoPE). To bridge the architecture gap, we introduced novel gating structures, careful initialization alignment, and staged distillation involving both token-level and hidden-state mimicry.
---
### **Challenges Faced**
- **Stability in Early Training:**
Unlike Transformer models, RWKV's state dynamics require careful gating and normalization. Without it, token dropout or state explosion frequently occurred during warm-up.
- **Cross-Architecture Distillation:**
Aligning a recurrent architecture with a feed-forward Transformer introduced step-wise divergence, especially in conversational jumps. Custom loss functions were employed to match hidden trajectories and long-term behavior, not just per-token outputs.
- **Context Sensitivity:**
Increasing context length beyond 2048 revealed stability cliffs. Careful adjustment of temporal decay, positional mixing, and memory routing was necessary to reach 3072 tokens reliably.
---
### **Current Limitations**
This is a *preview* version. The model is capable of coherent generation, especially in long-form settings, but may still show deviations in precision-demanding tasks or rare contexts. Prompt injection robustness and RLHF alignment are future work.
---
### **License & Usage**
This model is intended for **research and experimentation only**. Please consult the licensing terms of Qwen3 and RWKV if you intend to use this model commercially or fine-tune it.
---
### **Poem – The Cost of Curiosity**
> Countless times we failed—
> A ghost in the gradients,
> A silence in the state.
>
> Attention was easy.
> But ease never leads to breakthrough.
>
> We drank too much coffee.
> Slept too little.
>
> And somewhere between the hallucinations,
> The loss spikes,
> And the whispered curses at 3am—
>
> A new mind was born.
> PRWKV-7 lives.
---
2025 OpenMOSE
https://x.com/_m0se_ |
fats-fme/e7022a06-8423-4490-9934-13f3adf6b973 | fats-fme | 2025-05-03T06:43:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-7B-Instruct",
"base_model:adapter:unsloth/Qwen2-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T06:15:57Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e7022a06-8423-4490-9934-13f3adf6b973
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 576d05a318e5dd05_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/576d05a318e5dd05_train_data.json
type:
field_instruction: problem
field_output: reasoning_solution
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/e7022a06-8423-4490-9934-13f3adf6b973
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /tmp/576d05a318e5dd05_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c571c6f1-2bf0-403b-8085-e6a964a4f9c8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c571c6f1-2bf0-403b-8085-e6a964a4f9c8
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# e7022a06-8423-4490-9934-13f3adf6b973
This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 0.9034 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
aleegis/7b58edbb-d88d-4565-b281-4eff324bc672 | aleegis | 2025-05-03T06:38:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"region:us"
] | null | 2025-05-03T05:17:43Z | ---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7b58edbb-d88d-4565-b281-4eff324bc672
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 79ae7482d8ea96ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79ae7482d8ea96ee_train_data.json
type:
field_instruction: text
field_output: completion_a
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/7b58edbb-d88d-4565-b281-4eff324bc672
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/79ae7482d8ea96ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: f234d8d9-7843-44ae-80fb-4dccf66214cc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f234d8d9-7843-44ae-80fb-4dccf66214cc
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# 7b58edbb-d88d-4565-b281-4eff324bc672
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the None 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Saptarshi1234/starcoder2-3b-finetuned | Saptarshi1234 | 2025-05-03T06:36:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T06:35:56Z | ---
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] |
fivedoctors/ppo-SnowballTarget | fivedoctors | 2025-05-03T06:31:47Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2025-05-03T06:09:23Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: fivedoctors/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF | mradermacher | 2025-05-03T06:29:26Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:sequelbox/Qwen3-8B-Esper3-PREVIEW",
"base_model:quantized:sequelbox/Qwen3-8B-Esper3-PREVIEW",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T21:37:04Z | ---
base_model: sequelbox/Qwen3-8B-Esper3-PREVIEW
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/sequelbox/Qwen3-8B-Esper3-PREVIEW
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-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/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-Esper3-PREVIEW-GGUF/resolve/main/Qwen3-8B-Esper3-PREVIEW.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Alphatao/4e0d509f-42a3-4378-85fc-ef2fb7c82f27 | Alphatao | 2025-05-03T06:18:01Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/codegemma-7b-it",
"base_model:finetune:unsloth/codegemma-7b-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T05:25:34Z | ---
base_model: unsloth/codegemma-7b-it
library_name: transformers
model_name: 4e0d509f-42a3-4378-85fc-ef2fb7c82f27
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 4e0d509f-42a3-4378-85fc-ef2fb7c82f27
This model is a fine-tuned version of [unsloth/codegemma-7b-it](https://huggingface.co/unsloth/codegemma-7b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/4e0d509f-42a3-4378-85fc-ef2fb7c82f27", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/pcqut8h9)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
esuna/chelsea-minimal | esuna | 2025-05-03T06:10:01Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T06:10:00Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# chelsea-minimal
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
No trigger words defined.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
TOMFORD79/Fly35 | TOMFORD79 | 2025-05-03T06:09:35Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-03T05:46:54Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
alin13/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_moist_ocelot | alin13 | 2025-05-03T06:09:02Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am woolly moist ocelot",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-20T10:18:03Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_moist_ocelot
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am woolly moist ocelot
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_moist_ocelot
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="alin13/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-woolly_moist_ocelot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e | shibajustfor | 2025-05-03T06:06:08Z | 0 | 0 | transformers | [
"transformers",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T06:05:23Z | ---
library_name: transformers
model_name: shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e
tags:
- generated_from_trainer
licence: license
---
# Model Card for shibajustfor/3345c74b-ab00-4a6f-ad04-4478968f921e
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
sanabar/roberta-goemo-journals | sanabar | 2025-05-03T06:05:41Z | 65 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:SamLowe/roberta-base-go_emotions",
"base_model:finetune:SamLowe/roberta-base-go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-17T00:19:52Z | ---
library_name: transformers
license: mit
base_model: SamLowe/roberta-base-go_emotions
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: roberta-goemo-journals
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. -->
# roberta-goemo-journals
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
TadN427/NewTad | TadN427 | 2025-05-03T06:04:06Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T06:04:05Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK_tad
---
# Newtad
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK_tad` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK_tad",
"lora_weights": "https://huggingface.co/TadN427/NewTad/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('TadN427/NewTad', weight_name='lora.safetensors')
image = pipeline('TOK_tad').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/TadN427/NewTad/discussions) to add images that show off what you’ve made with this LoRA.
|
DuongTrongChi/qwen2.5-it-sft-v1-test | DuongTrongChi | 2025-05-03T06:01:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-1.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T06:00:57Z | ---
base_model: unsloth/Qwen2.5-1.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DuongTrongChi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-1.5B-Instruct
This qwen2 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)
|
mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF | mradermacher | 2025-05-03T06:00:13Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:grounded-ai/phi3.5-hallucination-judge-merge",
"base_model:quantized:grounded-ai/phi3.5-hallucination-judge-merge",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T03:04:51Z | ---
base_model: grounded-ai/phi3.5-hallucination-judge-merge
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/grounded-ai/phi3.5-hallucination-judge-merge
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-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/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ1_M.gguf) | i1-IQ1_M | 1.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ2_S.gguf) | i1-IQ2_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ2_M.gguf) | i1-IQ2_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ3_S.gguf) | i1-IQ3_S | 1.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ3_M.gguf) | i1-IQ3_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.3 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q4_0.gguf) | i1-Q4_0 | 2.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q4_1.gguf) | i1-Q4_1 | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/phi3.5-hallucination-judge-merge-i1-GGUF/resolve/main/phi3.5-hallucination-judge-merge.i1-Q6_K.gguf) | i1-Q6_K | 3.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
XformAI-india/qwen-0.6b-reasoning | XformAI-india | 2025-05-03T05:59:27Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"reasoning",
"dataset:openai/gsm8k",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:mit",
"region:us"
] | null | 2025-05-03T05:38:30Z | ---
license: mit
datasets:
- openai/gsm8k
base_model:
- Qwen/Qwen3-0.6B
tags:
- reasoning
---
# 🧠 Qwen-0.6B Reasoning – XformAI Fine-Tuned Model
**Model:** `XformAI-india/qwen-0.6b-reasoning`
**Base Model:** [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B)
**Architecture:** Transformer decoder (GPT-style)
**Fine-Tuned By:** [XformAI](https://xformai.in)
**Release Date:** May 2025
**License:** MIT
---
## 🧠 What is it?
`qwen-0.6b-reasoning` is a **compact transformer model fine-tuned for reasoning, logic, and analytical thinking**.
Despite its size, it demonstrates strong performance across:
- 🧩 Riddles & Puzzles
- 🧮 Math Word Problems
- 🧠 Symbolic Reasoning
- 💬 Chain-of-Thought Prompting
- 🔍 Common Sense Logic
> Fine-tuned on a curated instruction-style dataset focused on multi-step reasoning.
---
## 🚀 Why it Matters
- Performs like a **7B model** on reasoning benchmarks
- **Lightweight (600M)** and can run on CPU or mobile edge devices
- Excels in **step-by-step explanations** and **problem solving**
---
## 🧪 Fine-Tuning Overview
----------------------------------------------------------
| Category | Detail |
|----------------------|----------------------------------|
| Base Model | Qwen 0.6B |
| Target Objective | Reasoning, logic, CoT |
| Fine-Tuning Type | Instruction |
| Optimizer | AdamW (LoRA tuning) |
| Precision | bfloat16 |
| Epochs | 2 |
| Max Tokens | 2048 |
---
## 🧩 Prompt Example
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XformAI-india/qwen-0.6b-reasoning")
tokenizer = AutoTokenizer.from_pretrained("XformAI-india/qwen-0.6b-reasoning")
prompt = "A farmer has 17 sheep. All but 9 run away. How many are left?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
mwalker22/AIE6-S09-b99b3324-c1e6-4624-bf18-42f7d114c011 | mwalker22 | 2025-05-03T05:48:06Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:157",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:Snowflake/snowflake-arctic-embed-l",
"base_model:finetune:Snowflake/snowflake-arctic-embed-l",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-03T05:47:20Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:157
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: Snowflake/snowflake-arctic-embed-l
widget:
- source_sentence: How does the author describe Apple’s current LLM features compared
to frontier LLM capabilities?
sentences:
- 'Those US export regulations on GPUs to China seem to have inspired some very
effective training optimizations!
The environmental impact got better
A welcome result of the increased efficiency of the models—both the hosted ones
and the ones I can run locally—is that the energy usage and environmental impact
of running a prompt has dropped enormously over the past couple of years.
OpenAI themselves are charging 100x less for a prompt compared to the GPT-3 days.
I have it on good authority that neither Google Gemini nor Amazon Nova (two of
the least expensive model providers) are running prompts at a loss.'
- 'Now that those features are rolling out they’re pretty weak. As an LLM power-user
I know what these models are capable of, and Apple’s LLM features offer a pale
imitation of what a frontier LLM can do. Instead we’re getting notification summaries
that misrepresent news headlines and writing assistant tools that I’ve not found
useful at all. Genmoji are kind of fun though.
The rise of inference-scaling “reasoning” models
The most interesting development in the final quarter of 2024 was the introduction
of a new shape of LLM, exemplified by OpenAI’s o1 models—initially released as
o1-preview and o1-mini on September 12th.'
- 'A year ago, the only organization that had released a generally useful LLM was
OpenAI. We’ve now seen better-than-GPT-3 class models produced by Anthropic, Mistral,
Google, Meta, EleutherAI, Stability AI, TII in Abu Dhabi (Falcon), Microsoft Research,
xAI, Replit, Baidu and a bunch of other organizations.
The training cost (hardware and electricity) is still significant—initially millions
of dollars, but that seems to have dropped to the tens of thousands already. Microsoft’s
Phi-2 claims to have used “14 days on 96 A100 GPUs”, which works out at around
$35,000 using current Lambda pricing.'
- source_sentence: What topics are covered in the articles related to GPT and LLMs
in the provided context?
sentences:
- 'We already knew LLMs were spookily good at writing code. If you prompt them right,
it turns out they can build you a full interactive application using HTML, CSS
and JavaScript (and tools like React if you wire up some extra supporting build
mechanisms)—often in a single prompt.
Anthropic kicked this idea into high gear when they released Claude Artifacts,
a groundbreaking new feature that was initially slightly lost in the noise due
to being described half way through their announcement of the incredible Claude
3.5 Sonnet.
With Artifacts, Claude can write you an on-demand interactive application and
then let you use it directly inside the Claude interface.
Here’s my Extract URLs app, entirely generated by Claude:'
- 'Embeddings: What they are and why they matter
61.7k
79.3k
Catching up on the weird world of LLMs
61.6k
85.9k
llamafile is the new best way to run an LLM on your own computer
52k
66k
Prompt injection explained, with video, slides, and a transcript
51k
61.9k
AI-enhanced development makes me more ambitious with my projects
49.6k
60.1k
Understanding GPT tokenizers
49.5k
61.1k
Exploring GPTs: ChatGPT in a trench coat?
46.4k
58.5k
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
40.5k
49.2k
How to implement Q&A against your documentation with GPT3, embeddings and Datasette
37.3k
44.9k
Lawyer cites fake cases invented by ChatGPT, judge is not amused
37.1k
47.4k'
- 'Things we learned about LLMs in 2024
Simon Willison’s Weblog
Subscribe
Things we learned about LLMs in 2024
31st December 2024
A lot has happened in the world of Large Language Models over the course of 2024.
Here’s a review of things we figured out about the field in the past twelve months,
plus my attempt at identifying key themes and pivotal moments.
This is a sequel to my review of 2023.
In this article:'
- source_sentence: How do longer inputs enhance the problem-solving capabilities of
a large language model (LLM)?
sentences:
- 'Embeddings: What they are and why they matter
61.7k
79.3k
Catching up on the weird world of LLMs
61.6k
85.9k
llamafile is the new best way to run an LLM on your own computer
52k
66k
Prompt injection explained, with video, slides, and a transcript
51k
61.9k
AI-enhanced development makes me more ambitious with my projects
49.6k
60.1k
Understanding GPT tokenizers
49.5k
61.1k
Exploring GPTs: ChatGPT in a trench coat?
46.4k
58.5k
Could you train a ChatGPT-beating model for $85,000 and run it in a browser?
40.5k
49.2k
How to implement Q&A against your documentation with GPT3, embeddings and Datasette
37.3k
44.9k
Lawyer cites fake cases invented by ChatGPT, judge is not amused
37.1k
47.4k'
- 'Longer inputs dramatically increase the scope of problems that can be solved
with an LLM: you can now throw in an entire book and ask questions about its contents,
but more importantly you can feed in a lot of example code to help the model correctly
solve a coding problem. LLM use-cases that involve long inputs are far more interesting
to me than short prompts that rely purely on the information already baked into
the model weights. Many of my tools were built using this pattern.'
- 'I’ve found myself using this a lot. I noticed how much I was relying on it in
October and wrote Everything I built with Claude Artifacts this week, describing
14 little tools I had put together in a seven day period.
Since then, a whole bunch of other teams have built similar systems. GitHub announced
their version of this—GitHub Spark—in October. Mistral Chat added it as a feature
called Canvas in November.
Steve Krouse from Val Town built a version of it against Cerebras, showcasing
how a 2,000 token/second LLM can iterate on an application with changes visible
in less than a second.'
- source_sentence: What was the significance of the GPT-4 barrier mentioned in the
December 2023 review?
sentences:
- 'The environmental impact got much, much worse
The much bigger problem here is the enormous competitive buildout of the infrastructure
that is imagined to be necessary for these models in the future.
Companies like Google, Meta, Microsoft and Amazon are all spending billions of
dollars rolling out new datacenters, with a very material impact on the electricity
grid and the environment. There’s even talk of spinning up new nuclear power stations,
but those can take decades.
Is this infrastructure necessary? DeepSeek v3’s $6m training cost and the continued
crash in LLM prices might hint that it’s not. But would you want to be the big
tech executive that argued NOT to build out this infrastructure only to be proven
wrong in a few years’ time?'
- 'The May 13th announcement of GPT-4o included a demo of a brand new voice mode,
where the true multi-modal GPT-4o (the o is for “omni”) model could accept audio
input and output incredibly realistic sounding speech without needing separate
TTS or STT models.
The demo also sounded conspicuously similar to Scarlett Johansson... and after
she complained the voice from the demo, Skye, never made it to a production product.
The delay in releasing the new voice mode after the initial demo caused quite
a lot of confusion. I wrote about that in ChatGPT in “4o” mode is not running
the new features yet.'
- 'The GPT-4 barrier was comprehensively broken
In my December 2023 review I wrote about how We don’t yet know how to build GPT-4—OpenAI’s
best model was almost a year old at that point, yet no other AI lab had produced
anything better. What did OpenAI know that the rest of us didn’t?
I’m relieved that this has changed completely in the past twelve months. 18 organizations
now have models on the Chatbot Arena Leaderboard that rank higher than the original
GPT-4 from March 2023 (GPT-4-0314 on the board)—70 models in total.'
- source_sentence: What licensing model does Qwen25-Coder-32B use?
sentences:
- 'Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac talks about
Qwen2.5-Coder-32B in November—an Apache 2.0 licensed model!
I can now run a GPT-4 class model on my laptop talks about running Meta’s Llama
3.3 70B (released in December)'
- 'Except... you can run generated code to see if it’s correct. And with patterns
like ChatGPT Code Interpreter the LLM can execute the code itself, process the
error message, then rewrite it and keep trying until it works!
So hallucination is a much lesser problem for code generation than for anything
else. If only we had the equivalent of Code Interpreter for fact-checking natural
language!
How should we feel about this as software engineers?
On the one hand, this feels like a threat: who needs a programmer if ChatGPT can
write code for you?'
- 'The GPT-4 barrier was comprehensively broken
Some of those GPT-4 models run on my laptop
LLM prices crashed, thanks to competition and increased efficiency
Multimodal vision is common, audio and video are starting to emerge
Voice and live camera mode are science fiction come to life
Prompt driven app generation is a commodity already
Universal access to the best models lasted for just a few short months
“Agents” still haven’t really happened yet
Evals really matter
Apple Intelligence is bad, Apple’s MLX library is excellent
The rise of inference-scaling “reasoning” models
Was the best currently available LLM trained in China for less than $6m?
The environmental impact got better
The environmental impact got much, much worse'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.9166666666666666
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 1.0
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9166666666666666
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.20000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.10000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.9166666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 1.0
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9692441461309548
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9583333333333334
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9583333333333334
name: Cosine Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-l
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l) <!-- at revision d8fb21ca8d905d2832ee8b96c894d3298964346b -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("mwalker22/AIE6-S09-b99b3324-c1e6-4624-bf18-42f7d114c011")
# Run inference
sentences = [
'What licensing model does Qwen25-Coder-32B use?',
'Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac talks about Qwen2.5-Coder-32B in November—an Apache 2.0 licensed model!\n\nI can now run a GPT-4 class model on my laptop talks about running Meta’s Llama 3.3 70B (released in December)',
'The GPT-4 barrier was comprehensively broken\nSome of those GPT-4 models run on my laptop\nLLM prices crashed, thanks to competition and increased efficiency\nMultimodal vision is common, audio and video are starting to emerge\nVoice and live camera mode are science fiction come to life\nPrompt driven app generation is a commodity already\nUniversal access to the best models lasted for just a few short months\n“Agents” still haven’t really happened yet\nEvals really matter\nApple Intelligence is bad, Apple’s MLX library is excellent\nThe rise of inference-scaling “reasoning” models\nWas the best currently available LLM trained in China for less than $6m?\nThe environmental impact got better\nThe environmental impact got much, much worse',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
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<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9167 |
| cosine_accuracy@3 | 1.0 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.9167 |
| cosine_precision@3 | 0.3333 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.9167 |
| cosine_recall@3 | 1.0 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| **cosine_ndcg@10** | **0.9692** |
| cosine_mrr@10 | 0.9583 |
| cosine_map@100 | 0.9583 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 157 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 157 samples:
| | sentence_0 | sentence_1 |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 20.74 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 43 tokens</li><li>mean: 135.42 tokens</li><li>max: 214 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:----------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>When did Meta release the original Llama model?</code> | <code>Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.<br>I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!<br>This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.<br>Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.</code> |
| <code>What was significant about the release of Llama 2 in July?</code> | <code>Then in February, Meta released Llama. And a few weeks later in March, Georgi Gerganov released code that got it working on a MacBook.<br>I wrote about how Large language models are having their Stable Diffusion moment, and with hindsight that was a very good call!<br>This unleashed a whirlwind of innovation, which was accelerated further in July when Meta released Llama 2—an improved version which, crucially, included permission for commercial use.<br>Today there are literally thousands of LLMs that can be run locally, on all manner of different devices.</code> |
| <code>What is the new way to scale a model mentioned in the context?</code> | <code>The biggest innovation here is that it opens up a new way to scale a model: instead of improving model performance purely through additional compute at training time, models can now take on harder problems by spending more compute on inference.<br>The sequel to o1, o3 (they skipped “o2” for European trademark reasons) was announced on 20th December with an impressive result against the ARC-AGI benchmark, albeit one that likely involved more than $1,000,000 of compute time expense!<br>o3 is expected to ship in January. I doubt many people have real-world problems that would benefit from that level of compute expenditure—I certainly don’t!—but it appears to be a genuine next step in LLM architecture for taking on much harder problems.</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `num_train_epochs`: 10
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 10
- `per_device_eval_batch_size`: 10
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| 1.0 | 16 | 0.9583 |
| 2.0 | 32 | 0.9484 |
| 3.0 | 48 | 0.9539 |
| 3.125 | 50 | 0.9539 |
| 4.0 | 64 | 0.9692 |
| 5.0 | 80 | 0.9692 |
| 6.0 | 96 | 0.9692 |
| 6.25 | 100 | 0.9692 |
| 7.0 | 112 | 0.9692 |
| 8.0 | 128 | 0.9692 |
| 9.0 | 144 | 0.9692 |
| 9.375 | 150 | 0.9692 |
| 10.0 | 160 | 0.9692 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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liamfudge/gemma-3-1b-it-Q4_K_M-GGUF | liamfudge | 2025-05-03T05:46:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:google/gemma-3-1b-it",
"base_model:quantized:google/gemma-3-1b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-03T05:46:42Z | ---
base_model: google/gemma-3-1b-it
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
# liamfudge/gemma-3-1b-it-Q4_K_M-GGUF
This model was converted to GGUF format from [`google/gemma-3-1b-it`](https://huggingface.co/google/gemma-3-1b-it) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/google/gemma-3-1b-it) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo liamfudge/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo liamfudge/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo liamfudge/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo liamfudge/gemma-3-1b-it-Q4_K_M-GGUF --hf-file gemma-3-1b-it-q4_k_m.gguf -c 2048
```
|
18-Jobz-Hunting-Sajal-Malik-Viral-VideoX/NEW.EXCLUSIVE.Jobz.Hunting.Sajal.Malik.viral.video.Tutorial | 18-Jobz-Hunting-Sajal-Malik-Viral-VideoX | 2025-05-03T05:42:37Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T05:41:49Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5n7shfr3?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Actor jobz hunting sajal malik Original V𝚒deo V𝚒deo took the internet by storm and amazed viewers on various social media platforms. Actor jobz hunting sajal malik, a young and talented digital creator, recently became famous thanks to this interesting V𝚒deo.
L𝚎aked V𝚒deo Actor jobz hunting sajal malik V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media Telegram X Trending Tiktok |
Hachipo/Qwen2.5-7B-PIFT-jaen_1000_2 | Hachipo | 2025-05-03T05:39:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T05:34:54Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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] |
ivangrapher/e613ef31-81b5-45a5-ac2e-93cd24c7392c | ivangrapher | 2025-05-03T05:37:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T05:18:10Z | ---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e613ef31-81b5-45a5-ac2e-93cd24c7392c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 79ae7482d8ea96ee_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/79ae7482d8ea96ee_train_data.json
type:
field_instruction: text
field_output: completion_a
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: ivangrapher/e613ef31-81b5-45a5-ac2e-93cd24c7392c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/79ae7482d8ea96ee_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: f234d8d9-7843-44ae-80fb-4dccf66214cc
wandb_project: s56-7
wandb_run: your_name
wandb_runid: f234d8d9-7843-44ae-80fb-4dccf66214cc
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# e613ef31-81b5-45a5-ac2e-93cd24c7392c
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.127 | 0.1335 | 150 | 1.4296 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
SmallDoge/Qwen2.5-math-14b-llmlingua-90 | SmallDoge | 2025-05-03T05:32:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:59:43Z | ---
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] |
jahyungu/Qwen2.5-7B-Instruct_MetaMathQA-40K_random | jahyungu | 2025-05-03T05:31:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T01:20:03Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen2.5-7B-Instruct_MetaMathQA-40K_random
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. -->
# Qwen2.5-7B-Instruct_MetaMathQA-40K_random
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
abduraziq/assign3 | abduraziq | 2025-05-03T05:23:28Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T04:51:19Z | # Contrast to Divide: self-supervised pre-training for learning with noisy labels
[](https://paperswithcode.com/sota/image-classification-on-mini-webvision-1-0?p=contrast-to-divide-self-supervised-pre-1) [](https://paperswithcode.com/sota/image-classification-on-clothing1m?p=contrast-to-divide-self-supervised-pre-1)
This is an official implementation of
"Contrast to Divide: self-supervised pre-training for learning with noisy labels".
The code is based on [DivideMix](https://github.com/LiJunnan1992/DivideMix) implementation.
## Results
Following tables summarize main results of the paper:
CIFAR-10:
CIFAR-100:
Clothing1M:
mini-WebVision:
## Running the code
First you need to install dependencies by running `pip install -r requirements.txt`.
You can download pretrained self-supervised models from
[Google Drive](https://drive.google.com/drive/folders/1qYVdggtNFQZBZ-OqVJm80LBKUKpdLPUm?usp=sharing).
Alternatively, you can train them by yourself, using [SimCLR implementation](https://github.com/HobbitLong/SupContrast).
Put them into `./pretrained` folder.
Then you can run the code for CIFAR
```
python3 main_cifar.py --r 0.8 --lambda_u 500 --dataset cifar100 --p_threshold 0.03 --data_path ./cifar-100 --experiment-name simclr_resnet18 --method selfsup --net resnet50
```
for Clothing1M
```
python3 main_clothing1M.py --data_path /path/to/clothing1m --experiment-name selfsup --method selfsup --p_threshold 0.7 --warmup 5 --num_epochs 120
```
or for mini-WebVision
```
python3 Train_webvision.py --p_threshold 0.03 --num_class 50 --data_path /path/to/webvision --imagenet_data_path /path/to/imagenet --method selfsup```
```
To run C2D with ELR+ just use the self-suprevised pretrained models with the original [code](https://github.com/shengliu66/ELR/).
## License
This project is licensed under the terms of the MIT license.
|
tsaksatara73/dfv | tsaksatara73 | 2025-05-03T05:22:41Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-03T05:22:40Z | ---
license: creativeml-openrail-m
---
|
Hachipo/Qwen2.5-7B-PIFT-enja_1000_2 | Hachipo | 2025-05-03T05:22:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T05:17:51Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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] |
aleegis/7f0760f8-8862-4787-b7a3-74b614fd0238 | aleegis | 2025-05-03T05:15:11Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"region:us"
] | null | 2025-05-03T03:57:04Z | ---
library_name: peft
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7f0760f8-8862-4787-b7a3-74b614fd0238
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: elyza/Llama-3-ELYZA-JP-8B
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 13b16be7f737d1a4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/13b16be7f737d1a4_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/7f0760f8-8862-4787-b7a3-74b614fd0238
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/13b16be7f737d1a4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: a15fa850-4ddf-4312-aec2-39afd0e9a706
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a15fa850-4ddf-4312-aec2-39afd0e9a706
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# 7f0760f8-8862-4787-b7a3-74b614fd0238
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
New-VIRAL-Gangu-chettri-7-2-kanda-video-li/NAPALI.Gangu.Chettri.Kanda.7.2.Video.OFICIAL.link | New-VIRAL-Gangu-chettri-7-2-kanda-video-li | 2025-05-03T04:58:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T03:43:35Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
penelitianpsmatematika/medical-classification-t5-small-v3 | penelitianpsmatematika | 2025-05-03T04:49:46Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-29T15:07:22Z | ---
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] |
mlfoundations-dev/no_pipeline_science_30k | mlfoundations-dev | 2025-05-03T04:47:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T22:47:50Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: no_pipeline_science_30k
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. -->
# no_pipeline_science_30k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/no_pipeline_science_30k 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
ayushchakravarthy/phi4-mini-instruct-s1-sft | ayushchakravarthy | 2025-05-03T04:46:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:48:29Z | ---
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] |
ayushchakravarthy/qwen3-0.6b-base-s1-sft | ayushchakravarthy | 2025-05-03T04:45:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:53:33Z | ---
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] |
XzWang/ruozhiReasoner-Qwen3-8B | XzWang | 2025-05-03T04:44:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T04:38:15Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
### 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
AthenaAgent42/llama-r1-ft13k-ex3 | AthenaAgent42 | 2025-05-03T04:44:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T04:44:20Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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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[More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
jmalejandrob79/nbmaexp01 | jmalejandrob79 | 2025-05-03T04:42:36Z | 3 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-02T02:36:46Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: nbmaexp01
---
# Nbmaexp01
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `nbmaexp01` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nbmaexp01",
"lora_weights": "https://huggingface.co/jmalejandrob79/nbmaexp01/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jmalejandrob79/nbmaexp01', weight_name='lora.safetensors')
image = pipeline('nbmaexp01').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jmalejandrob79/nbmaexp01/discussions) to add images that show off what you’ve made with this LoRA.
|
keplersystems/kepler-urdu-poetry-tiny | keplersystems | 2025-05-03T04:28:07Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"region:us"
] | text-generation | 2025-05-03T01:26:00Z | ---
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
--- |
era-temporary/eb-man-7b-stage2-after-stage1-lr-1e-5-lora-e2 | era-temporary | 2025-05-03T04:24:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct",
"region:us"
] | null | 2025-05-03T04:23:01Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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### Framework versions
- PEFT 0.15.2 |
sophiayk20/blip-gqa-ft-trial2 | sophiayk20 | 2025-05-03T04:23:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"blip-2",
"visual-question-answering",
"generated_from_trainer",
"base_model:Salesforce/blip2-opt-2.7b",
"base_model:finetune:Salesforce/blip2-opt-2.7b",
"license:mit",
"endpoints_compatible",
"region:us"
] | visual-question-answering | 2025-05-02T22:18:14Z | ---
library_name: transformers
license: mit
base_model: Salesforce/blip2-opt-2.7b
tags:
- generated_from_trainer
model-index:
- name: blip-gqa-ft-trial2
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. -->
# blip-gqa-ft-trial2
This model is a fine-tuned version of [Salesforce/blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0074 | 1.0 | 313 | 2.0112 |
| 1.7625 | 2.0 | 626 | 1.9272 |
| 1.853 | 3.0 | 939 | 1.8629 |
| 1.6087 | 4.0 | 1252 | 1.8508 |
| 1.6017 | 4.9856 | 1560 | 1.8559 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.4.0+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
|
BABYSHARK09/New58 | BABYSHARK09 | 2025-05-03T04:18:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:01:07Z | ---
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] |
earcherc/girl1 | earcherc | 2025-05-03T04:14:09Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-03T04:12:08Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/ComfyICU_00001_.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# girl1
<Gallery />
## Model description
First attempt LoRA
## Download model
Weights for this model are available in Safetensors format.
[Download](/earcherc/girl1/tree/main) them in the Files & versions tab.
|
cyberbabooshka/post_pretrain_pre_cooldown | cyberbabooshka | 2025-05-03T04:11:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"axolotl",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T04:11:11Z | ---
library_name: transformers
tags:
- axolotl
---
# 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] |
fats-fme/11813507-b1af-412e-a487-858d4ea24855 | fats-fme | 2025-05-03T04:08:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"license:llama3",
"region:us"
] | null | 2025-05-03T03:59:43Z | ---
library_name: peft
license: llama3
base_model: elyza/Llama-3-ELYZA-JP-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 11813507-b1af-412e-a487-858d4ea24855
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: elyza/Llama-3-ELYZA-JP-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 13b16be7f737d1a4_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/13b16be7f737d1a4_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 16
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/11813507-b1af-412e-a487-858d4ea24855
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 50
micro_batch_size: 1
mlflow_experiment_name: /tmp/13b16be7f737d1a4_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a15fa850-4ddf-4312-aec2-39afd0e9a706
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a15fa850-4ddf-4312-aec2-39afd0e9a706
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 11813507-b1af-412e-a487-858d4ea24855
This model is a fine-tuned version of [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0012 | 1 | 1.1470 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
BABYSHARK09/New56 | BABYSHARK09 | 2025-05-03T04:07:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:00:54Z | ---
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] |
KushGupster/granite-3-flux-1-Q8_0-GGUF | KushGupster | 2025-05-03T04:06:08Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:KushGupster/granite-3-flux-1",
"base_model:quantized:KushGupster/granite-3-flux-1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T04:05:23Z | ---
base_model: KushGupster/granite-3-flux-1
tags:
- llama-cpp
- gguf-my-repo
---
# KushGupster/granite-3-flux-1-Q8_0-GGUF
This model was converted to GGUF format from [`KushGupster/granite-3-flux-1`](https://huggingface.co/KushGupster/granite-3-flux-1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/KushGupster/granite-3-flux-1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo KushGupster/granite-3-flux-1-Q8_0-GGUF --hf-file granite-3-flux-1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo KushGupster/granite-3-flux-1-Q8_0-GGUF --hf-file granite-3-flux-1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo KushGupster/granite-3-flux-1-Q8_0-GGUF --hf-file granite-3-flux-1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo KushGupster/granite-3-flux-1-Q8_0-GGUF --hf-file granite-3-flux-1-q8_0.gguf -c 2048
```
|
Zack-Z/gemma3_27bi_cotsft_rs0_2_5cut_gem3all_e2 | Zack-Z | 2025-05-03T04:02:05Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-27b-it",
"base_model:finetune:unsloth/gemma-3-27b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T01:44:54Z | ---
base_model: unsloth/gemma-3-27b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Zack-Z
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-27b-it
This gemma3 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)
|
penelitianpsmatematika/medical-text-generation-t5-small-v1 | penelitianpsmatematika | 2025-05-03T03:59:13Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-29T08:53:11Z | ---
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] |
NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF | NikolayKozloff | 2025-05-03T03:47:54Z | 0 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:kalomaze/Qwen3-16B-A3B",
"base_model:quantized:kalomaze/Qwen3-16B-A3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T03:47:07Z | ---
base_model: kalomaze/Qwen3-16B-A3B
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF
This model was converted to GGUF format from [`kalomaze/Qwen3-16B-A3B`](https://huggingface.co/kalomaze/Qwen3-16B-A3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/kalomaze/Qwen3-16B-A3B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Qwen3-16B-A3B-Q5_K_S-GGUF --hf-file qwen3-16b-a3b-q5_k_s.gguf -c 2048
```
|
RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf | RichardErkhov | 2025-05-03T03:44:58Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T01:42:21Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3_it_dpo_list_and_bold - GGUF
- Model creator: https://huggingface.co/1231czx/
- Original model: https://huggingface.co/1231czx/llama3_it_dpo_list_and_bold/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama3_it_dpo_list_and_bold.Q2_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q2_K.gguf) | Q2_K | 2.96GB |
| [llama3_it_dpo_list_and_bold.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [llama3_it_dpo_list_and_bold.IQ3_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [llama3_it_dpo_list_and_bold.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [llama3_it_dpo_list_and_bold.IQ3_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [llama3_it_dpo_list_and_bold.Q3_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q3_K.gguf) | Q3_K | 3.74GB |
| [llama3_it_dpo_list_and_bold.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [llama3_it_dpo_list_and_bold.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [llama3_it_dpo_list_and_bold.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [llama3_it_dpo_list_and_bold.Q4_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q4_0.gguf) | Q4_0 | 4.34GB |
| [llama3_it_dpo_list_and_bold.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [llama3_it_dpo_list_and_bold.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [llama3_it_dpo_list_and_bold.Q4_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q4_K.gguf) | Q4_K | 4.58GB |
| [llama3_it_dpo_list_and_bold.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [llama3_it_dpo_list_and_bold.Q4_1.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q4_1.gguf) | Q4_1 | 4.78GB |
| [llama3_it_dpo_list_and_bold.Q5_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q5_0.gguf) | Q5_0 | 5.21GB |
| [llama3_it_dpo_list_and_bold.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [llama3_it_dpo_list_and_bold.Q5_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q5_K.gguf) | Q5_K | 5.34GB |
| [llama3_it_dpo_list_and_bold.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [llama3_it_dpo_list_and_bold.Q5_1.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q5_1.gguf) | Q5_1 | 5.65GB |
| [llama3_it_dpo_list_and_bold.Q6_K.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q6_K.gguf) | Q6_K | 6.14GB |
| [llama3_it_dpo_list_and_bold.Q8_0.gguf](https://huggingface.co/RichardErkhov/1231czx_-_llama3_it_dpo_list_and_bold-gguf/blob/main/llama3_it_dpo_list_and_bold.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
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BABYSHARK09/New47 | BABYSHARK09 | 2025-05-03T03:37:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:59:55Z | ---
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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BABYSHARK09/New48 | BABYSHARK09 | 2025-05-03T03:37:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:00:02Z | ---
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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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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[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
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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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#### 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).
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DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF | DoppelReflEx | 2025-05-03T03:34:32Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:DoppelReflEx/MiniusLight-24B-v2.2a-test",
"base_model:quantized:DoppelReflEx/MiniusLight-24B-v2.2a-test",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T03:33:29Z | ---
base_model: DoppelReflEx/MiniusLight-24B-v2.2a-test
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF
This model was converted to GGUF format from [`DoppelReflEx/MiniusLight-24B-v2.2a-test`](https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.2a-test) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DoppelReflEx/MiniusLight-24B-v2.2a-test) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo DoppelReflEx/MiniusLight-24B-v2.2a-test-Q4_K_S-GGUF --hf-file miniuslight-24b-v2.2a-test-q4_k_s.gguf -c 2048
```
|
grimjim/MagTie-v1-12B | grimjim | 2025-05-03T03:31:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2311.03099",
"base_model:Delta-Vector/Francois-Huali-12B",
"base_model:merge:Delta-Vector/Francois-Huali-12B",
"base_model:grimjim/Magnolia-v3-12B",
"base_model:merge:grimjim/Magnolia-v3-12B",
"base_model:grimjim/mistralai-Mistral-Nemo-Base-2407",
"base_model:merge:grimjim/mistralai-Mistral-Nemo-Base-2407",
"base_model:inflatebot/MN-12B-Mag-Mell-R1",
"base_model:merge:inflatebot/MN-12B-Mag-Mell-R1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:47:26Z | ---
base_model:
- Delta-Vector/Francois-Huali-12B
- grimjim/mistralai-Mistral-Nemo-Base-2407
- grimjim/Magnolia-v3-12B
- inflatebot/MN-12B-Mag-Mell-R1
library_name: transformers
pipeline_tag: text-generation
tags:
- mergekit
- merge
license: apache-2.0
---
# MagTie-v1-12B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
We used a pretrained base model as the base for a DARE-TIES merge, compensating by boosting the weights and densities in order to retain more training from the contributing models.
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [grimjim/mistralai-Mistral-Nemo-Base-2407](https://huggingface.co/grimjim/mistralai-Mistral-Nemo-Base-2407) as a base.
### Models Merged
The following models were included in the merge:
* [Delta-Vector/Francois-Huali-12B](https://huggingface.co/Delta-Vector/Francois-Huali-12B)
* [grimjim/Magnolia-v3-12B](https://huggingface.co/grimjim/Magnolia-v3-12B)
* [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: grimjim/mistralai-Mistral-Nemo-Base-2407
models:
- model: grimjim/mistralai-Mistral-Nemo-Base-2407
- model: inflatebot/MN-12B-Mag-Mell-R1
parameters:
weight: 0.85
density: 0.75
- model: Delta-Vector/Francois-Huali-12B
parameters:
weight: 0.85
density: 0.75
- model: grimjim/Magnolia-v3-12B
parameters:
weight: 0.85
density: 0.75
merge_method: dare_ties
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
|
hesiii/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soft_tame_condor | hesiii | 2025-05-03T03:30:56Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am soft tame condor",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-14T23:37:57Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soft_tame_condor
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am soft tame condor
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soft_tame_condor
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hesiii/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-soft_tame_condor", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
punitub01/llama2-7b-qlora-finetuned | punitub01 | 2025-05-03T03:29:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T03:28:58Z | ---
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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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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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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## Model Card Contact
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BABYSHARK09/New45 | BABYSHARK09 | 2025-05-03T03:25:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:59:43Z | ---
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]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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**APA:**
[More Information Needed]
## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
NoorNizar/Phi-4-mini-instruct-WINT4 | NoorNizar | 2025-05-03T03:25:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"llmcompressor",
"quantization",
"wint4",
"conversational",
"custom_code",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] | text-generation | 2025-05-03T03:23:31Z | ---
library_name: transformers
tags:
- llmcompressor
- quantization
- wint4
---
# Phi-4-mini-instruct-WINT4
This model is a 4-bit quantized version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct) "using the [llmcompressor](https://github.com/neuralmagic/llmcompressor) library.
## Quantization Details
* **Base Model:** [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct)
* **Quantization Library:** `llmcompressor`
* **Quantization Method:** Weight-only 4-bit int (WINT4)
* **Quantization Recipe:**
```yaml
quant_stage:
quant_modifiers:
QuantizationModifier:
ignore: [lm_head]
config_groups:
group_0:
weights: {num_bits: 4, type: int, symmetric: true, strategy: channel, dynamic: false}
targets: [Linear]
```
## Evaluation Results
The following table shows the evaluation results on various benchmarks compared to the baseline (non-quantized) model.
| Task | Baseline Metric (10.0% Threshold) | Quantized Metric | Metric Type |
|------------------|-------------------------------------------------------|------------------|---------------------|
| winogrande | 0.7545 | 0.6985 | acc,none |
## How to Use
You can load the quantized model and tokenizer using the `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "NoorNizar/Phi-4-mini-instruct-WINT4"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Example usage (replace with your specific task)
prompt = "Hello, world!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Disclaimer
This model was quantized automatically using a script. Performance and behavior might differ slightly from the original base model.
|
mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF | mradermacher | 2025-05-03T03:21:38Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu",
"base_model:quantized:IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T21:18:45Z | ---
base_model: IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu
language: en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/IntelLabs/sqft-sparsepeft-phi-3-mini-4k-30-math-heu
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-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/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_S.gguf) | Q3_K_S | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_M.gguf) | Q3_K_M | 2.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.IQ4_XS.gguf) | IQ4_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q3_K_L.gguf) | Q3_K_L | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q4_K_S.gguf) | Q4_K_S | 2.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q4_K_M.gguf) | Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q5_K_S.gguf) | Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q6_K.gguf) | Q6_K | 3.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/sqft-sparsepeft-phi-3-mini-4k-30-math-heu-GGUF/resolve/main/sqft-sparsepeft-phi-3-mini-4k-30-math-heu.f16.gguf) | f16 | 7.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
toilahonganh1712/tinyllama-bnb-4bit-travelvungtau360 | toilahonganh1712 | 2025-05-03T03:16:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T03:16:27Z | ---
base_model: unsloth/tinyllama-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** toilahonganh1712
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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)
|
thavens-research/Qwen2.5-0.5B-Instruct | thavens-research | 2025-05-03T03:12:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T03:10:15Z | ---
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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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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#### Summary
## Model Examination [optional]
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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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sakhalif10/fluxoldvhseffect | sakhalif10 | 2025-05-03T03:10:14Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-05-03T03:10:09Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/VHS+Trailer+v3+4-3.00_00_48_26.Still001.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: apache-2.0
---
# vhs-old-effect-flux
<Gallery />
## Model description
this is my first flux loras
## Download model
[Download](/sakhalif10/fluxoldvhseffect/tree/main) them in the Files & versions tab.
|
memeviss/zombieIV_6 | memeviss | 2025-05-03T03:01:24Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T02:34:26Z | # Optimized TTS Model
This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques.
## Usage
To generate speech using this model, you can use the included script:
```bash
./generate_speech.py --text "Your text here" --output_path output.wav
```
For more details, see the optimization report in this directory.
|
mradermacher/Qwen3-32B-Uncensored-i1-GGUF | mradermacher | 2025-05-03T03:00:12Z | 0 | 3 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:Guilherme34/uncensor",
"base_model:nicoboss/Qwen3-32B-Uncensored",
"base_model:quantized:nicoboss/Qwen3-32B-Uncensored",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-02T23:34:19Z | ---
base_model: nicoboss/Qwen3-32B-Uncensored
datasets:
- Guilherme34/uncensor
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B/blob/main/LICENSE
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/nicoboss/Qwen3-32B-Uncensored
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-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/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ1_M.gguf) | i1-IQ1_M | 8.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ2_S.gguf) | i1-IQ2_S | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ2_M.gguf) | i1-IQ2_M | 11.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-i1-GGUF/resolve/main/Qwen3-32B-Uncensored.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MinaMila/phi3_unlearned_LoRa_ACSEmployment_2_cfda_ep6_22 | MinaMila | 2025-05-03T02:55:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T02:55:11Z | ---
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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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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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- **Compute Region:** [More Information Needed]
- **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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<!-- 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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Membersuger/Euro_5 | Membersuger | 2025-05-03T02:54:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:31:54Z | ---
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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[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. -->
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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]
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flyingbugs/Qwen2.5-Math-7B-generalthoughts-0.5-token-prune | flyingbugs | 2025-05-03T02:52:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T21:20:03Z | ---
base_model: Qwen/Qwen2.5-Math-7B-Instruct
datasets: flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune
library_name: transformers
model_name: Qwen2.5-Math-7B-generalthoughts-0.5-token-prune
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-Math-7B-generalthoughts-0.5-token-prune
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune](https://huggingface.co/datasets/flyingbugs/GeneralThought-195K-pruned-keep-0.5-token-prune) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="flyingbugs/Qwen2.5-Math-7B-generalthoughts-0.5-token-prune", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jjh233/huggingface/runs/5bizs4qo)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Alphatao/3c248048-d823-41e8-acd1-08b0985334a5 | Alphatao | 2025-05-03T02:50:45Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2.5-Math-1.5B",
"base_model:finetune:unsloth/Qwen2.5-Math-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T23:39:22Z | ---
base_model: unsloth/Qwen2.5-Math-1.5B
library_name: transformers
model_name: 3c248048-d823-41e8-acd1-08b0985334a5
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 3c248048-d823-41e8-acd1-08b0985334a5
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/3c248048-d823-41e8-acd1-08b0985334a5", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/qrfff2s8)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
chchen/MentaLLaMA-chat-7B-PsyCourse-doc-info-fold9 | chchen | 2025-05-03T02:50:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:klyang/MentaLLaMA-chat-7B-hf",
"base_model:adapter:klyang/MentaLLaMA-chat-7B-hf",
"license:mit",
"region:us"
] | null | 2025-05-03T01:14:11Z | ---
library_name: peft
license: mit
base_model: klyang/MentaLLaMA-chat-7B-hf
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: MentaLLaMA-chat-7B-PsyCourse-doc-info-fold9
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. -->
# MentaLLaMA-chat-7B-PsyCourse-doc-info-fold9
This model is a fine-tuned version of [klyang/MentaLLaMA-chat-7B-hf](https://huggingface.co/klyang/MentaLLaMA-chat-7B-hf) on the course-doc-info-train-fold9 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3604 | 0.3951 | 10 | 0.3692 |
| 1.0978 | 0.7901 | 20 | 0.2423 |
| 0.1519 | 1.1852 | 30 | 0.1737 |
| 0.1384 | 1.5802 | 40 | 0.1437 |
| 0.1076 | 1.9753 | 50 | 0.1253 |
| 0.1085 | 2.3704 | 60 | 0.1120 |
| 0.0884 | 2.7654 | 70 | 0.1006 |
| 0.1071 | 3.1605 | 80 | 0.0919 |
| 0.0761 | 3.5556 | 90 | 0.0892 |
| 0.0661 | 3.9506 | 100 | 0.0851 |
| 0.0532 | 4.3457 | 110 | 0.0835 |
| 0.0653 | 4.7407 | 120 | 0.0834 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
jnjj/instruction-model | jnjj | 2025-05-03T02:46:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T21:20:36Z | ---
library_name: transformers
--- |
luckycanucky/discord_model_x3_16b | luckycanucky | 2025-05-03T02:45:52Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:42:36Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** luckycanucky
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
jahyungu/Llama-3.2-1B-Instruct_Open-Critic-GPT_9 | jahyungu | 2025-05-03T02:41:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:31:41Z | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct_Open-Critic-GPT_9
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. -->
# Llama-3.2-1B-Instruct_Open-Critic-GPT_9
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
LandCruiser/sn21_omegav1_0305_1 | LandCruiser | 2025-05-03T02:35:24Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-03T02:18:57Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
jhyun0414/20250503-Llama-3.1-8B-Instruct-gemini_label-filter-e3-lr2e-06 | jhyun0414 | 2025-05-03T02:32:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:26:09Z | ---
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] |
kejones/results | kejones | 2025-05-03T02:27:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-02T19:25:43Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
ivangrapher/fbb5f401-ffa7-4787-93e4-bc6e09a1450e | ivangrapher | 2025-05-03T02:24:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T02:19:18Z | ---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fbb5f401-ffa7-4787-93e4-bc6e09a1450e
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 3293ce73be5009ec_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3293ce73be5009ec_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: ivangrapher/fbb5f401-ffa7-4787-93e4-bc6e09a1450e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/3293ce73be5009ec_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a0ab280a-c85a-410f-a2fe-19bf02a514ec
wandb_project: s56-7
wandb_run: your_name
wandb_runid: a0ab280a-c85a-410f-a2fe-19bf02a514ec
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# fbb5f401-ffa7-4787-93e4-bc6e09a1450e
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8713
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9193 | 0.0853 | 150 | 0.8713 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
BenevolenceMessiah/Qwen3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF | BenevolenceMessiah | 2025-05-03T02:21:53Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES",
"base_model:quantized:BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T02:21:41Z | ---
base_model: BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF
This model was converted to GGUF format from [`BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES`](https://huggingface.co/BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF --hf-file qwen-3-14b-enhanced-v1.0-dare-ties-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF --hf-file qwen-3-14b-enhanced-v1.0-dare-ties-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF --hf-file qwen-3-14b-enhanced-v1.0-dare-ties-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BenevolenceMessiah/Qwen-3-14B-Enhanced-v1.0-DARE-TIES-Q8_0-GGUF --hf-file qwen-3-14b-enhanced-v1.0-dare-ties-q8_0.gguf -c 2048
```
|
vermoney/7d485ece-7d7a-4c1c-8d11-676bd95a0643 | vermoney | 2025-05-03T02:21:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Llama-3.2-1B",
"base_model:adapter:NousResearch/Llama-3.2-1B",
"license:llama3.2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T02:19:39Z | ---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7d485ece-7d7a-4c1c-8d11-676bd95a0643
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3293ce73be5009ec_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3293ce73be5009ec_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vermoney/7d485ece-7d7a-4c1c-8d11-676bd95a0643
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/3293ce73be5009ec_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a0ab280a-c85a-410f-a2fe-19bf02a514ec
wandb_project: s56-9
wandb_run: your_name
wandb_runid: a0ab280a-c85a-410f-a2fe-19bf02a514ec
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7d485ece-7d7a-4c1c-8d11-676bd95a0643
This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8508
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8896 | 0.1138 | 200 | 0.8508 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
jairosolare/ArashaLalani_biglust16_LoRa | jairosolare | 2025-05-03T02:20:58Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T02:19:45Z | sdxl lora
trained on biglust 1.6
works well with DMD2 lora
sampler: lcm Karras
weight: 1.0-ish
steps:10-14
trigger= celeb name |
ellietang/hf_saved_lora_amf-modCase-qwen-coder-14B-SFT-after-CPT-try1-no-SYSTEM_PROMPT | ellietang | 2025-05-03T02:19:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T02:19:25Z | ---
library_name: transformers
tags:
- unsloth
---
# 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] |
BenevolenceMessiah/Qwen3-14B-Enhanced-v1.0-DARE-TIES | BenevolenceMessiah | 2025-05-03T02:19:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:Ba2han/Qwen-3-14B-Gemini-v0.1",
"base_model:merge:Ba2han/Qwen-3-14B-Gemini-v0.1",
"base_model:Qwen/Qwen3-14B",
"base_model:merge:Qwen/Qwen3-14B",
"base_model:secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b",
"base_model:merge:secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T02:17:28Z | ---
base_model:
- Ba2han/Qwen-3-14B-Gemini-v0.1
- secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b
- Qwen/Qwen3-14B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) as a base.
### Models Merged
The following models were included in the merge:
* [Ba2han/Qwen-3-14B-Gemini-v0.1](https://huggingface.co/Ba2han/Qwen-3-14B-Gemini-v0.1)
* [secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b](https://huggingface.co/secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
# Qwen-3-14B-Enhanced-v1.0-DARE-TIES
merge_method: dare_ties
base_model: Qwen/Qwen3-14B
parameters:
density: 0.333
random_seed: 37
models:
- model: secmlr/SWE-BENCH-5k-first-2000-claude-search-replace-generation-qwen_3_14b
parameters:
weight: 0.5
- model: Ba2han/Qwen-3-14B-Gemini-v0.1
parameters:
weight: 0.5
tokenizer:
source: union
chat_template: auto
dtype: bfloat16
```
|
mradermacher/Qwen3-32B-Uncensored-GGUF | mradermacher | 2025-05-03T02:18:49Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"dataset:Guilherme34/uncensor",
"base_model:nicoboss/Qwen3-32B-Uncensored",
"base_model:quantized:nicoboss/Qwen3-32B-Uncensored",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T23:05:43Z | ---
base_model: nicoboss/Qwen3-32B-Uncensored
datasets:
- Guilherme34/uncensor
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-32B/blob/main/LICENSE
quantized_by: mradermacher
tags:
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/nicoboss/Qwen3-32B-Uncensored
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-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/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q3_K_M.gguf) | Q3_K_M | 16.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q4_K_M.gguf) | Q4_K_M | 19.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q5_K_M.gguf) | Q5_K_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-32B-Uncensored-GGUF/resolve/main/Qwen3-32B-Uncensored.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
smrc/fr-qc-turbo-omg-token | smrc | 2025-05-03T02:17:33Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T02:17:28Z | ---
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] |
jairosolare/SabrinaCarpenter_biglust16_LoRa | jairosolare | 2025-05-03T02:16:37Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T02:15:17Z | sdxl lora
trained on biglust 1.6
works well with DMD2 lora
sampler: lcm Karras
weight: 1.0-ish
steps:10-14
trigger= celeb name |
jairosolare/DishaPatani_biglust16_LoRa | jairosolare | 2025-05-03T02:12:06Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T02:09:43Z | sdxl lora
trained on biglust 1.6
works well with DMD2 lora
sampler: lcm Karras
weight: 1.0-ish
steps:10-14
trigger= celeb name
credit to creator: https://civitai.com/models/1421562/disha-patani-sdxl?modelVersionId=1606785 |
Subsets and Splits