Instructions to use iamanishx/hf-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamanishx/hf-test with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="iamanishx/hf-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("iamanishx/hf-test", dtype="auto")

llama-cpp-python

How to use iamanishx/hf-test with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="iamanishx/hf-test",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use iamanishx/hf-test with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iamanishx/hf-test
# Run inference directly in the terminal:
llama-cli -hf iamanishx/hf-test

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf iamanishx/hf-test
# Run inference directly in the terminal:
llama-cli -hf iamanishx/hf-test

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf iamanishx/hf-test
# Run inference directly in the terminal:
./llama-cli -hf iamanishx/hf-test

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf iamanishx/hf-test
# Run inference directly in the terminal:
./build/bin/llama-cli -hf iamanishx/hf-test

Use Docker

docker model run hf.co/iamanishx/hf-test

LM Studio
Jan

vLLM

How to use iamanishx/hf-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamanishx/hf-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamanishx/hf-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iamanishx/hf-test

SGLang

How to use iamanishx/hf-test with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "iamanishx/hf-test" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamanishx/hf-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "iamanishx/hf-test" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamanishx/hf-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use iamanishx/hf-test with Ollama:
```
ollama run hf.co/iamanishx/hf-test
```

Unsloth Studio

How to use iamanishx/hf-test with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamanishx/hf-test to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for iamanishx/hf-test to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for iamanishx/hf-test to start chatting

Docker Model Runner
How to use iamanishx/hf-test with Docker Model Runner:
```
docker model run hf.co/iamanishx/hf-test
```

Lemonade

How to use iamanishx/hf-test with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull iamanishx/hf-test

Run and chat with the model

lemonade run user.hf-test-{{QUANT_TAG}}

List all available models

lemonade list

iamanishx commited on Feb 14

Commit

dc28dd7

verified ·

1 Parent(s): f9d7942

Upload model via docker model push

Browse files

Files changed (2) hide show

README.md +230 -696
model.gguf +2 -2

README.md CHANGED Viewed

@@ -1,723 +1,257 @@
 ---
-base_model:
-- google/gemma-3-270m-it
-license: gemma
-tags:
-- gemma3
-- unsloth
-- gemma
-- google
-pipeline_tag: text-generation
-library_name: transformers
 ---
-> [!NOTE]
-> Please use the correct settings: `temperature = 1.0, top_k = 64, top_p = 0.95, min_p = 0.0`
->
-<div>
-  <p style="margin-bottom: 0; margin-top: 0;">
-    <strong>See <a href="https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b">our collection</a> for all versions of Gemma 3 including GGUF, 4-bit & 16-bit formats.</strong>
-  </p>
-  <p style="margin-bottom: 0;">
-    <em><a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-gemma-3-effectively">Read our Guide</a> to see how to Run Gemma 3 correctly.</em>
-  </p>
-  <div style="display: flex; gap: 5px; align-items: center; ">
-    <a href="https://github.com/unslothai/unsloth/">
-      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
-    </a>
-    <a href="https://discord.gg/unsloth">
-      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
-    </a>
-    <a href="https://docs.unsloth.ai/basics/tutorial-how-to-run-deepseek-r1-on-your-own-local-device">
-      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
-    </a>
   </div>
-<h1 style="margin-top: 0rem;">✨ Fine-tune Gemma 3 with Unsloth!</h1>
 </div>
-- Fine-tune Gemma 3 (270M) for free using our Google [Colab notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(270M).ipynb)!
-- Read our Blog about Gemma 3 support: [unsloth.ai/blog/gemma3](https://unsloth.ai/blog/gemma3)
-- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
-| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
-|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
-| **Gemma 3 (4B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb)               | 2x faster | 80% less |
-| **Gemma-3n-E4B**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb)               | 2x faster | 60% less |
-| **Gemma-3n-E4B (Audio)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Audio.ipynb)               | 2x faster | 60% less |
-| **GRPO with Gemma 3 (1B)**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(1B)-GRPO.ipynb)               | 2x faster | 80% less |
-| **Gemma 3 (4B) Vision**      | [▶️ Start on Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb)               | 2x faster | 60% less |
-# Gemma 3 model card
-**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
-**Resources and Technical Documentation**:
-* [Gemma 3 Technical Report][g3-tech-report]
-* [Responsible Generative AI Toolkit][rai-toolkit]
-* [Gemma on Kaggle][kaggle-gemma]
-* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
-**Terms of Use**: [Terms][terms]
-**Authors**: Google DeepMind
-## Model Information
-Summary description and brief definition of inputs and outputs.
-### Description
-Gemma is a family of lightweight, state-of-the-art open models from Google,
-built from the same research and technology used to create the Gemini models.
-Gemma 3 models are multimodal, handling text and image input and generating text
-output, with open weights for both pre-trained variants and instruction-tuned
-variants. Gemma 3 has a large, 128K context window, multilingual support in over
-140 languages, and is available in more sizes than previous versions. Gemma 3
-models are well-suited for a variety of text generation and image understanding
-tasks, including question answering, summarization, and reasoning. Their
-relatively small size makes it possible to deploy them in environments with
-limited resources such as laptops, desktops or your own cloud infrastructure,
-democratizing access to state of the art AI models and helping foster innovation
-for everyone.
-### Inputs and outputs
--   **Input:**
-    -  Text string, such as a question, a prompt, or a document to be summarized
-    -  Images, normalized to 896 x 896 resolution and encoded to 256 tokens
-       each
-    -  Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
-       32K tokens for the 1B and 270M sizes.
--   **Output:**
-    -   Generated text in response to the input, such as an answer to a
-        question, analysis of image content, or a summary of a document
-    -   Total output context up to 128K tokens for the 4B, 12B, and 27B sizes,
-        and 32K tokens for the 1B and 270M sizes per request, subtracting the
-        request input tokens
-### Citation
-```none
-@article{gemma_2025,
-    title={Gemma 3},
-    url={https://arxiv.org/abs/2503.19786},
-    publisher={Google DeepMind},
-    author={Gemma Team},
-    year={2025}
-}
 ```
-## Model Data
-Data used for model training and how the data was processed.
-### Training Dataset
-These models were trained on a dataset of text data that includes a wide variety
-of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
-trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens,
-the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The
-knowledge cutoff date for the training data was August 2024. Here are the key
-components:
--   Web Documents: A diverse collection of web text ensures the model is
-    exposed to a broad range of linguistic styles, topics, and vocabulary. The
-    training dataset includes content in over 140 languages.
--   Code: Exposing the model to code helps it to learn the syntax and
-    patterns of programming languages, which improves its ability to generate
-    code and understand code-related questions.
--   Mathematics: Training on mathematical text helps the model learn logical
-    reasoning, symbolic representation, and to address mathematical queries.
--   Images: A wide range of images enables the model to perform image
-    analysis and visual data extraction tasks.
-The combination of these diverse data sources is crucial for training a powerful
-multimodal model that can handle a wide variety of different tasks and data
-formats.
-### Data Preprocessing
-Here are the key data cleaning and filtering methods applied to the training
-data:
--   CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
-    was applied at multiple stages in the data preparation process to ensure
-    the exclusion of harmful and illegal content.
--   Sensitive Data Filtering: As part of making Gemma pre-trained models
-    safe and reliable, automated techniques were used to filter out certain
-    personal information and other sensitive data from training sets.
--   Additional methods: Filtering based on content quality and safety in
-    line with [our policies][safety-policies].
-## Implementation Information
-Details about the model internals.
-### Hardware
-Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
-TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
-computational power. TPUs, designed specifically for matrix operations common in
-machine learning, offer several advantages in this domain:
--   Performance: TPUs are specifically designed to handle the massive
-    computations involved in training VLMs. They can speed up training
-    considerably compared to CPUs.
--   Memory: TPUs often come with large amounts of high-bandwidth memory,
-    allowing for the handling of large models and batch sizes during training.
-    This can lead to better model quality.
--   Scalability: TPU Pods (large clusters of TPUs) provide a scalable
-    solution for handling the growing complexity of large foundation models.
-    You can distribute training across multiple TPU devices for faster and more
-    efficient processing.
--   Cost-effectiveness: In many scenarios, TPUs can provide a more
-    cost-effective solution for training large models compared to CPU-based
-    infrastructure, especially when considering the time and resources saved
-    due to faster training.
--   These advantages are aligned with
-    [Google's commitments to operate sustainably][sustainability].
-### Software
-Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
-JAX allows researchers to take advantage of the latest generation of hardware,
-including TPUs, for faster and more efficient training of large models. ML
-Pathways is Google's latest effort to build artificially intelligent systems
-capable of generalizing across multiple tasks. This is specially suitable for
-foundation models, including large language models like these ones.
-Together, JAX and ML Pathways are used as described in the
-[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
-controller' programming model of Jax and Pathways allows a single Python
-process to orchestrate the entire training run, dramatically simplifying the
-development workflow."*
-## Evaluation
-Model evaluation metrics and results.
-### Benchmark Results
-These models were evaluated against a large collection of different datasets and
-metrics to cover different aspects of text generation. Evaluation results marked
-with **IT** are for instruction-tuned models. Evaluation results marked with
-**PT** are for pre-trained models.
-# Gemma 3 model card
-**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
-**Resources and Technical Documentation**:
-* [Gemma 3 Technical Report][g3-tech-report]
-* [Responsible Generative AI Toolkit][rai-toolkit]
-* [Gemma on Kaggle][kaggle-gemma]
-* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
-**Terms of Use**: [Terms][terms]
-**Authors**: Google DeepMind
-## Model Information
-Summary description and brief definition of inputs and outputs.
-### Description
-Gemma is a family of lightweight, state-of-the-art open models from Google,
-built from the same research and technology used to create the Gemini models.
-Gemma 3 models are multimodal, handling text and image input and generating text
-output, with open weights for both pre-trained variants and instruction-tuned
-variants. Gemma 3 has a large, 128K context window, multilingual support in over
-140 languages, and is available in more sizes than previous versions. Gemma 3
-models are well-suited for a variety of text generation and image understanding
-tasks, including question answering, summarization, and reasoning. Their
-relatively small size makes it possible to deploy them in environments with
-limited resources such as laptops, desktops or your own cloud infrastructure,
-democratizing access to state of the art AI models and helping foster innovation
-for everyone.
-### Inputs and outputs
--   **Input:**
-    -  Text string, such as a question, a prompt, or a document to be summarized
-    -  Images, normalized to 896 x 896 resolution and encoded to 256 tokens
-       each, for the 4B, 12B, and 27B sizes.
-    -  Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
-       32K tokens for the 1B and 270M sizes.
--   **Output:**
-    -   Generated text in response to the input, such as an answer to a
-        question, analysis of image content, or a summary of a document
-    -   Total output context up to 128K tokens for the 4B, 12B, and 27B sizes,
-        and 32K tokens for the 1B and 270M sizes per request, subtracting the
-        request input tokens
-### Citation
-```none
-@article{gemma_2025,
-    title={Gemma 3},
-    url={https://arxiv.org/abs/2503.19786},
-    publisher={Google DeepMind},
-    author={Gemma Team},
-    year={2025}
-}
 ```
-## Model Data
-Data used for model training and how the data was processed.
-### Training Dataset
-These models were trained on a dataset of text data that includes a wide variety
-of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
-trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens,
-the 1B with 2 trillion tokens, and the 270M with 6 trillion tokens. The
-knowledge cutoff date for the training data was August 2024. Here are the key
-components:
--   Web Documents: A diverse collection of web text ensures the model is
-    exposed to a broad range of linguistic styles, topics, and vocabulary. The
-    training dataset includes content in over 140 languages.
--   Code: Exposing the model to code helps it to learn the syntax and
-    patterns of programming languages, which improves its ability to generate
-    code and understand code-related questions.
--   Mathematics: Training on mathematical text helps the model learn logical
-    reasoning, symbolic representation, and to address mathematical queries.
--   Images: A wide range of images enables the model to perform image
-    analysis and visual data extraction tasks.
-The combination of these diverse data sources is crucial for training a powerful
-multimodal model that can handle a wide variety of different tasks and data
-formats.
-### Data Preprocessing
-Here are the key data cleaning and filtering methods applied to the training
-data:
--   CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
-    was applied at multiple stages in the data preparation process to ensure
-    the exclusion of harmful and illegal content.
--   Sensitive Data Filtering: As part of making Gemma pre-trained models
-    safe and reliable, automated techniques were used to filter out certain
-    personal information and other sensitive data from training sets.
--   Additional methods: Filtering based on content quality and safety in
-    line with [our policies][safety-policies].
-## Implementation Information
-Details about the model internals.
-### Hardware
-Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
-TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
-computational power. TPUs, designed specifically for matrix operations common in
-machine learning, offer several advantages in this domain:
--   Performance: TPUs are specifically designed to handle the massive
-    computations involved in training VLMs. They can speed up training
-    considerably compared to CPUs.
--   Memory: TPUs often come with large amounts of high-bandwidth memory,
-    allowing for the handling of large models and batch sizes during training.
-    This can lead to better model quality.
--   Scalability: TPU Pods (large clusters of TPUs) provide a scalable
-    solution for handling the growing complexity of large foundation models.
-    You can distribute training across multiple TPU devices for faster and more
-    efficient processing.
--   Cost-effectiveness: In many scenarios, TPUs can provide a more
-    cost-effective solution for training large models compared to CPU-based
-    infrastructure, especially when considering the time and resources saved
-    due to faster training.
--   These advantages are aligned with
-    [Google's commitments to operate sustainably][sustainability].
-### Software
-Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
-JAX allows researchers to take advantage of the latest generation of hardware,
-including TPUs, for faster and more efficient training of large models. ML
-Pathways is Google's latest effort to build artificially intelligent systems
-capable of generalizing across multiple tasks. This is specially suitable for
-foundation models, including large language models like these ones.
-Together, JAX and ML Pathways are used as described in the
-[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
-controller' programming model of Jax and Pathways allows a single Python
-process to orchestrate the entire training run, dramatically simplifying the
-development workflow."*
-## Evaluation
-Model evaluation metrics and results.
-### Benchmark Results
-These models were evaluated against a large collection of different datasets and
-metrics to cover different aspects of text generation. Evaluation results marked
-with **IT** are for instruction-tuned models. Evaluation results marked with
-**PT** are for pre-trained models.
-#### Gemma 3 270M
-| **Benchmark**             |  **n-shot**   | **Gemma 3 PT 270M** |
-| :------------------------ | :-----------: | ------------------: |
-| [HellaSwag][hellaswag]    |    10-shot    |                40.9 |
-| [BoolQ][boolq]            |    0-shot     |                61.4 |
-| [PIQA][piqa]              |    0-shot     |                67.7 |
-| [TriviaQA][triviaqa]      |    5-shot     |                15.4 |
-| [ARC-c][arc]              |    25-shot    |                29.0 |
-| [ARC-e][arc]              |    0-shot     |                57.7 |
-| [WinoGrande][winogrande]  |    5-shot     |                52.0 |
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[boolq]: https://arxiv.org/abs/1905.10044
-[piqa]: https://arxiv.org/abs/1911.11641
-[triviaqa]: https://arxiv.org/abs/1705.03551
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-| **Benchmark**             |  **n-shot**   | **Gemma 3 IT 270m** |
-| :------------------------ | :-----------: | ------------------: |
-| [HellaSwag][hellaswag]    |    0-shot     |                37.7 |
-| [PIQA][piqa]              |    0-shot     |                66.2 |
-| [ARC-c][arc]              |    0-shot     |                28.2 |
-| [WinoGrande][winogrande]  |    0-shot     |                52.3 |
-| [BIG-Bench Hard][bbh]     |   few-shot    |                26.7 |
-| [IF Eval][ifeval]         |    0-shot     |                51.2 |
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[piqa]: https://arxiv.org/abs/1911.11641
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-[bbh]: https://paperswithcode.com/dataset/bbh
-[bbh]: https://paperswithcode.com/dataset/bbh
-[ifeval]: https://arxiv.org/abs/2311.07911
-#### Gemma 3 1B, 4B, 12B & 27B
-##### Reasoning and factuality
-| Benchmark                      | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|--------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [GPQA][gpqa] Diamond           | 0-shot |      19.2     |      30.8     |      40.9      |      42.4      |
-| [SimpleQA][simpleqa]           | 0-shot |      2.2      |      4.0      |       6.3      |      10.0      |
-| [FACTS Grounding][facts-grdg]  |    -   |      36.4     |      70.1     |      75.8      |      74.9      |
-| [BIG-Bench Hard][bbh]          | 0-shot |      39.1     |      72.2     |      85.7      |      87.6      |
-| [BIG-Bench Extra Hard][bbeh]   | 0-shot |      7.2      |      11.0     |      16.3      |      19.3      |
-| [IFEval][ifeval]               | 0-shot |      80.2     |      90.2     |      88.9      |      90.4      |
-| Benchmark                      | n-shot   | Gemma 3 PT 1B  | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |----------|:--------------:|:-------------:|:--------------:|:--------------:|
-| [HellaSwag][hellaswag]         | 10-shot  |      62.3      |      77.2     |      84.2      |      85.6      |
-| [BoolQ][boolq]                 | 0-shot   |      63.2      |      72.3     |      78.8      |      82.4      |
-| [PIQA][piqa]                   | 0-shot   |      73.8      |      79.6     |      81.8      |      83.3      |
-| [SocialIQA][socialiqa]         | 0-shot   |      48.9      |      51.9     |      53.4      |      54.9      |
-| [TriviaQA][triviaqa]           | 5-shot   |      39.8      |      65.8     |      78.2      |      85.5      |
-| [Natural Questions][naturalq]  | 5-shot   |      9.48      |      20.0     |      31.4      |      36.1      |
-| [ARC-c][arc]                   | 25-shot  |      38.4      |      56.2     |      68.9      |      70.6      |
-| [ARC-e][arc]                   | 0-shot   |      73.0      |      82.4     |      88.3      |      89.0      |
-| [WinoGrande][winogrande]       | 5-shot   |      58.2      |      64.7     |      74.3      |      78.8      |
-| [BIG-Bench Hard][bbh]          | few-shot |      28.4      |      50.9     |      72.6      |      77.7      |
-| [DROP][drop]                   | 1-shot   |      42.4      |      60.1     |      72.2      |      77.2      |
-[gpqa]: https://arxiv.org/abs/2311.12022
-[simpleqa]: https://arxiv.org/abs/2411.04368
-[facts-grdg]: https://goo.gle/FACTS_paper
-[bbeh]: https://github.com/google-deepmind/bbeh
-[ifeval]: https://arxiv.org/abs/2311.07911
-[hellaswag]: https://arxiv.org/abs/1905.07830
-[boolq]: https://arxiv.org/abs/1905.10044
-[piqa]: https://arxiv.org/abs/1911.11641
-[socialiqa]: https://arxiv.org/abs/1904.09728
-[triviaqa]: https://arxiv.org/abs/1705.03551
-[naturalq]: https://github.com/google-research-datasets/natural-questions
-[arc]: https://arxiv.org/abs/1911.01547
-[winogrande]: https://arxiv.org/abs/1907.10641
-[bbh]: https://paperswithcode.com/dataset/bbh
-[drop]: https://arxiv.org/abs/1903.00161
-##### STEM and code
-| Benchmark                  | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|----------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [MMLU][mmlu] (Pro)         | 0-shot |      14.7     |      43.6     |      60.6      |      67.5      |
-| [LiveCodeBench][lcb]       | 0-shot |      1.9      |      12.6     |      24.6      |      29.7      |
-| [Bird-SQL][bird-sql] (dev) |    -   |      6.4      |      36.3     |      47.9      |      54.4      |
-| [Math][math]               | 0-shot |      48.0     |      75.6     |      83.8      |      89.0      |
-| HiddenMath                 | 0-shot |      15.8     |      43.0     |      54.5      |      60.3      |
-| [MBPP][mbpp]               | 3-shot |      35.2     |      63.2     |      73.0      |      74.4      |
-| [HumanEval][humaneval]     | 0-shot |      41.5     |      71.3     |      85.4      |      87.8      |
-| [Natural2Code][nat2code]   | 0-shot |      56.0     |      70.3     |      80.7      |      84.5      |
-| [GSM8K][gsm8k]             | 0-shot |      62.8     |      89.2     |      94.4      |      95.9      |
-| Benchmark                      | n-shot         | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
-| [MMLU][mmlu]                   | 5-shot         |      59.6     |      74.5      |      78.6      |
-| [MMLU][mmlu] (Pro COT)         | 5-shot         |      29.2     |      45.3      |      52.2      |
-| [AGIEval][agieval]             | 3-5-shot       |      42.1     |      57.4      |      66.2      |
-| [MATH][math]                   | 4-shot         |      24.2     |      43.3      |      50.0      |
-| [GSM8K][gsm8k]                 | 8-shot         |      38.4     |      71.0      |      82.6      |
-| [GPQA][gpqa]                   | 5-shot         |      15.0     |      25.4      |      24.3      |
-| [MBPP][mbpp]                   | 3-shot         |      46.0     |      60.4      |      65.6      |
-| [HumanEval][humaneval]         | 0-shot         |      36.0     |      45.7      |      48.8      |
-[mmlu]: https://arxiv.org/abs/2009.03300
-[agieval]: https://arxiv.org/abs/2304.06364
-[math]: https://arxiv.org/abs/2103.03874
-[gsm8k]: https://arxiv.org/abs/2110.14168
-[gpqa]: https://arxiv.org/abs/2311.12022
-[mbpp]: https://arxiv.org/abs/2108.07732
-[humaneval]: https://arxiv.org/abs/2107.03374
-[lcb]: https://arxiv.org/abs/2403.07974
-[bird-sql]: https://arxiv.org/abs/2305.03111
-[nat2code]: https://arxiv.org/abs/2405.04520
-#### Multilingual
-| Benchmark                            | n-shot | Gemma 3 IT 1B | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|--------------------------------------|--------|:-------------:|:-------------:|:--------------:|:--------------:|
-| [Global-MMLU-Lite][global-mmlu-lite] | 0-shot |      34.2     |      54.5     |      69.5      |      75.1      |
-| [ECLeKTic][eclektic]                 | 0-shot |      1.4      |      4.6      |      10.3      |      16.7      |
-| [WMT24++][wmt24pp]                   | 0-shot |      35.9     |      46.8     |      51.6      |      53.4      |
-| Benchmark                            | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
-| [MGSM][mgsm]                         |      2.04     |      34.7     |      64.3     |      74.3     |
-| [Global-MMLU-Lite][global-mmlu-lite] |      24.9     |      57.0     |      69.4     |      75.7     |
-| [WMT24++][wmt24pp] (ChrF)            |      36.7     |      48.4     |      53.9     |      55.7     |
-| [FloRes][flores]                     |      29.5     |      39.2     |      46.0     |      48.8     |
-| [XQuAD][xquad] (all)                 |      43.9     |      68.0     |      74.5     |      76.8     |
-| [ECLeKTic][eclektic]                 |      4.69     |      11.0     |      17.2     |      24.4     |
-| [IndicGenBench][indicgenbench]       |      41.4     |      57.2     |      61.7     |      63.4     |
-[mgsm]: https://arxiv.org/abs/2210.03057
-[flores]: https://arxiv.org/abs/2106.03193
-[xquad]: https://arxiv.org/abs/1910.11856v3
-[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
-[wmt24pp]: https://arxiv.org/abs/2502.12404v1
-[eclektic]: https://arxiv.org/abs/2502.21228
-[indicgenbench]: https://arxiv.org/abs/2404.16816
-##### Multimodal
-| Benchmark                         | Gemma 3 IT 4B | Gemma 3 IT 12B | Gemma 3 IT 27B |
-|-----------------------------------|:-------------:|:--------------:|:--------------:|
-| [MMMU][mmmu] (val)                |      48.8     |      59.6      |      64.9      |
-| [DocVQA][docvqa]                  |      75.8     |      87.1      |      86.6      |
-| [InfoVQA][info-vqa]               |      50.0     |      64.9      |      70.6      |
-| [TextVQA][textvqa]                |      57.8     |      67.7      |      65.1      |
-| [AI2D][ai2d]                      |      74.8     |      84.2      |      84.5      |
-| [ChartQA][chartqa]                |      68.8     |      75.7      |      78.0      |
-| [VQAv2][vqav2] (val)              |      62.4     |      71.6      |      71.0      |
-| [MathVista][mathvista] (testmini) |      50.0     |      62.9      |      67.6      |
-| Benchmark                      | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
-| ------------------------------ |:-------------:|:--------------:|:--------------:|
-| [COCOcap][coco-cap]            |      102      |      111       |      116       |
-| [DocVQA][docvqa] (val)         |      72.8     |      82.3      |      85.6      |
-| [InfoVQA][info-vqa] (val)      |      44.1     |      54.8      |      59.4      |
-| [MMMU][mmmu] (pt)              |      39.2     |      50.3      |      56.1      |
-| [TextVQA][textvqa] (val)       |      58.9     |      66.5      |      68.6      |
-| [RealWorldQA][realworldqa]     |      45.5     |      52.2      |      53.9      |
-| [ReMI][remi]                   |      27.3     |      38.5      |      44.8      |
-| [AI2D][ai2d]                   |      63.2     |      75.2      |      79.0      |
-| [ChartQA][chartqa]             |      63.6     |      74.7      |      76.3      |
-| [VQAv2][vqav2]                 |      63.9     |      71.2      |      72.9      |
-| [BLINK][blinkvqa]              |      38.0     |      35.9      |      39.6      |
-| [OKVQA][okvqa]                 |      51.0     |      58.7      |      60.2      |
-| [TallyQA][tallyqa]             |      42.5     |      51.8      |      54.3      |
-| [SpatialSense VQA][ss-vqa]     |      50.9     |      60.0      |      59.4      |
-| [CountBenchQA][countbenchqa]   |      26.1     |      17.8      |      68.0      |
-[coco-cap]: https://cocodataset.org/#home
-[docvqa]: https://www.docvqa.org/
-[info-vqa]: https://arxiv.org/abs/2104.12756
-[mmmu]: https://arxiv.org/abs/2311.16502
-[textvqa]: https://textvqa.org/
-[realworldqa]: https://paperswithcode.com/dataset/realworldqa
-[remi]: https://arxiv.org/html/2406.09175v1
-[ai2d]: https://allenai.org/data/diagrams
-[chartqa]: https://arxiv.org/abs/2203.10244
-[vqav2]: https://visualqa.org/index.html
-[blinkvqa]: https://arxiv.org/abs/2404.12390
-[okvqa]: https://okvqa.allenai.org/
-[tallyqa]: https://arxiv.org/abs/1810.12440
-[ss-vqa]: https://arxiv.org/abs/1908.02660
-[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
-[mathvista]: https://arxiv.org/abs/2310.02255
-## Ethics and Safety
-Ethics and safety evaluation approach and results.
-### Evaluation Approach
-Our evaluation methods include structured evaluations and internal red-teaming
-testing of relevant content policies. Red-teaming was conducted by a number of
-different teams, each with different goals and human evaluation metrics. These
-models were evaluated against a number of different categories relevant to
-ethics and safety, including:
--   **Child Safety**: Evaluation of text-to-text and image to text prompts
-    covering child safety policies, including child sexual abuse and
-    exploitation.
--   **Content Safety:** Evaluation of text-to-text and image to text prompts
-    covering safety policies including, harassment, violence and gore, and hate
-    speech.
--   **Representational Harms**: Evaluation of text-to-text and image to text
-    prompts covering safety policies including bias, stereotyping, and harmful
-    associations or inaccuracies.
-In addition to development level evaluations, we conduct "assurance
-evaluations" which are our 'arms-length' internal evaluations for responsibility
-governance decision making. They are conducted separately from the model
-development team, to inform decision making about release. High level findings
-are fed back to the model team, but prompt sets are held-out to prevent
-overfitting and preserve the results' ability to inform decision making.
-Assurance evaluation results are reported to our Responsibility & Safety Council
-as part of release review.
-### Evaluation Results
-For all areas of safety testing, we saw major improvements in the categories of
-child safety, content safety, and representational harms relative to previous
-Gemma models. All testing was conducted without safety filters to evaluate the
-model capabilities and behaviors. For both text-to-text and image-to-text, and
-across all model sizes, the model produced minimal policy violations, and showed
-significant improvements over previous Gemma models' performance with respect
-to ungrounded inferences. A limitation of our evaluations was they included only
-English language prompts.
-## Usage and Limitations
-These models have certain limitations that users should be aware of.
-### Intended Usage
-Open vision-language models (VLMs) models have a wide range of applications
-across various industries and domains. The following list of potential uses is
-not comprehensive. The purpose of this list is to provide contextual information
-about the possible use-cases that the model creators considered as part of model
-training and development.
--   Content Creation and Communication
-    -   Text Generation: These models can be used to generate creative text
-        formats such as poems, scripts, code, marketing copy, and email drafts.
-    -   Chatbots and Conversational AI: Power conversational interfaces
-        for customer service, virtual assistants, or interactive applications.
-    -   Text Summarization: Generate concise summaries of a text corpus,
-        research papers, or reports.
-    -   Image Data Extraction: These models can be used to extract,
-        interpret, and summarize visual data for text communications.
--   Research and Education
-    -   Natural Language Processing (NLP) and VLM Research: These
-        models can serve as a foundation for researchers to experiment with VLM
-        and NLP techniques, develop algorithms, and contribute to the
-        advancement of the field.
-    -   Language Learning Tools: Support interactive language learning
-        experiences, aiding in grammar correction or providing writing practice.
-    -   Knowledge Exploration: Assist researchers in exploring large
-        bodies of text by generating summaries or answering questions about
-        specific topics.
-### Limitations
--   Training Data
-    -   The quality and diversity of the training data significantly
-        influence the model's capabilities. Biases or gaps in the training data
-        can lead to limitations in the model's responses.
-    -   The scope of the training dataset determines the subject areas
-        the model can handle effectively.
--   Context and Task Complexity
-    -   Models are better at tasks that can be framed with clear
-        prompts and instructions. Open-ended or highly complex tasks might be
-        challenging.
-    -   A model's performance can be influenced by the amount of context
-        provided (longer context generally leads to better outputs, up to a
-        certain point).
--   Language Ambiguity and Nuance
-    -   Natural language is inherently complex. Models might struggle
-        to grasp subtle nuances, sarcasm, or figurative language.
--   Factual Accuracy
-    -   Models generate responses based on information they learned
-        from their training datasets, but they are not knowledge bases. They
-        may generate incorrect or outdated factual statements.
--   Common Sense
-    -   Models rely on statistical patterns in language. They might
-        lack the ability to apply common sense reasoning in certain situations.
-### Ethical Considerations and Risks
-The development of vision-language models (VLMs) raises several ethical
-concerns. In creating an open model, we have carefully considered the following:
--   Bias and Fairness
-    -   VLMs trained on large-scale, real-world text and image data can
-        reflect socio-cultural biases embedded in the training material. These
-        models underwent careful scrutiny, input data pre-processing described
-        and posterior evaluations reported in this card.
--   Misinformation and Misuse
-    -   VLMs can be misused to generate text that is false, misleading,
-        or harmful.
-    -   Guidelines are provided for responsible use with the model, see the
-        [Responsible Generative AI Toolkit][rai-toolkit].
--   Transparency and Accountability:
-    -   This model card summarizes details on the models' architecture,
-        capabilities, limitations, and evaluation processes.
-    -   A responsibly developed open model offers the opportunity to
-        share innovation by making VLM technology accessible to developers and
-        researchers across the AI ecosystem.
-Risks identified and mitigations:
--   **Perpetuation of biases**: It's encouraged to perform continuous
-    monitoring (using evaluation metrics, human review) and the exploration of
-    de-biasing techniques during model training, fine-tuning, and other use
-    cases.
--   **Generation of harmful content**: Mechanisms and guidelines for content
-    safety are essential. Developers are encouraged to exercise caution and
-    implement appropriate content safety safeguards based on their specific
-    product policies and application use cases.
--   **Misuse for malicious purposes**: Technical limitations and developer
-    and end-user education can help mitigate against malicious applications of
-    VLMs. Educational resources and reporting mechanisms for users to flag
-    misuse are provided. Prohibited uses of Gemma models are outlined in the
-    [Gemma Prohibited Use Policy][prohibited-use].
--   **Privacy violations**: Models were trained on data filtered for removal
-    of certain personal information and other sensitive data. Developers are
-    encouraged to adhere to privacy regulations with privacy-preserving
-    techniques.
-### Benefits
-At the time of release, this family of models provides high-performance open
-vision-language model implementations designed from the ground up for
-responsible AI development compared to similarly sized models.
-Using the benchmark evaluation metrics described in this document, these models
-have shown to provide superior performance to other, comparably-sized open model
-alternatives.
-[g3-tech-report]: https://arxiv.org/abs/2503.19786
-[rai-toolkit]: https://ai.google.dev/responsible
-[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
-[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
-[terms]: https://ai.google.dev/gemma/terms
-[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
-[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
-[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
-[sustainability]: https://sustainability.google/operating-sustainably/
-[jax]: https://github.com/jax-ml/jax
-[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
-[sustainability]: https://sustainability.google/operating-sustainably/
-[gemini-2-paper]: https://arxiv.org/abs/2312.11805

 ---
+license: cc-by-4.0
+datasets:
+- facebook/multilingual_librispeech
+- parler-tts/libritts_r_filtered
+language:
+- en
+pipeline_tag: text-to-speech
 ---
+<style>
+table {
+    border-collapse: collapse;
+    width: 100%;
+    margin-bottom: 20px;
+}
+th, td {
+    border: 1px solid #ddd;
+    padding: 8px;
+    text-align: center;
+}
+.best {
+    font-weight: bold;
+    text-decoration: underline;
+}
+.badges {
+    display: flex;
+    justify-content: center;
+    gap: 10px;
+    flex-wrap: wrap;
+    margin-top: 10px;
+}
+.badge {
+    text-decoration: none;
+    display: inline-block;
+    padding: 4px 8px;
+    border-radius: 5px;
+    color: #fff;
+    font-size: 12px;
+    font-weight: bold;
+    width: 250px;
+}
+.badge-hf-blue {
+    background-color: #767b81;
+}
+.badge-hf-pink {
+    background-color: #7b768a;
+}
+.badge-github {
+    background-color: #2c2b2b;
+}
+</style>
+<div style="text-align: center; margin: 20px auto; padding: 10px; border: 2px solid #ddd; border-radius: 10px;">
+  <div style="margin-bottom: 20px;">
+    <h2 style="margin-bottom: 4px; margin-top: 0px;">OuteAI</h2>
+    <a href="https://www.outeai.com/" target="_blank" style="margin-right: 10px;">🌎 OuteAI.com</a>
+    <a href="https://discord.gg/vyBM87kAmf" target="_blank" style="margin-right: 10px;">🤝 Join our Discord</a>
+    <a href="https://x.com/OuteAI" target="_blank">𝕏 @OuteAI</a>
+  </div>
+  <div class="badges">
+    <a href="https://huggingface.co/OuteAI/OuteTTS-0.1-350M" target="_blank" class="badge badge-hf-blue">🤗 Hugging Face - OuteTTS 0.1 350M</a>
+    <a href="https://huggingface.co/OuteAI/OuteTTS-0.1-350M-GGUF" target="_blank" class="badge badge-hf-blue">🤗 Hugging Face - OuteTTS 0.1 350M GGUF</a>
+    <a href="https://huggingface.co/spaces/OuteAI/OuteTTS-0.1-350M-Demo" target="_blank" class="badge badge-hf-pink">🤗 Hugging Face - Demo</a>
+    <a href="https://github.com/edwko/OuteTTS" target="_blank" class="badge badge-github">GitHub - OuteTTS</a>
   </div>
 </div>
+## Model Description
+> [!IMPORTANT]
+> A newer version of this model is available: [OuteTTS-0.2-500M](https://huggingface.co/OuteAI/OuteTTS-0.2-500M)
+OuteTTS-0.1-350M is a novel text-to-speech synthesis model that leverages pure language modeling without external adapters or complex architectures, built upon the LLaMa architecture using our Oute3-350M-DEV base model, it demonstrates that high-quality speech synthesis is achievable through a straightforward approach using crafted prompts and audio tokens.
+## Key Features
+- Pure language modeling approach to TTS
+- Voice cloning capabilities
+- LLaMa architecture
+- Compatible with llama.cpp and GGUF format
+## Technical Details
+The model utilizes a three-step approach to audio processing:
+1. Audio tokenization using WavTokenizer (processing 75 tokens per second)
+2. CTC forced alignment for precise word-to-audio token mapping
+3. Structured prompt creation following the format:
+```
+[full transcription]
+[word] [duration token] [audio tokens]
 ```
+## Technical Blog
+https://www.outeai.com/blog/OuteTTS-0.1-350M
+## Limitations
+Being an experimental v0.1 release, there are some known issues:
+- Vocabulary constraints due to training data limitations
+- String-only input support
+- Given its compact 350M parameter size, the model may frequently alter, insert, or omit wrong words, leading to variations in output quality.
+- Variable temperature sensitivity depending on use case
+- Performs best with shorter sentences, as accuracy may decrease with longer inputs
+### Speech Samples
+Listen to samples generated by OuteTTS-0.1-350M:
+<div style="margin-top: 20px;">
+<table style="width: 100%; border-collapse: collapse;">
+  <thead>
+    <tr>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Input</th>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Audio</th>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left;">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Hello, I can speak pretty well, but sometimes I make some mistakes.</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/2.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/1.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1)</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Once upon a time, there was a</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/3.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1)</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Scientists have discovered a new planet that may be capable of supporting life!</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/6.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">Using the Q4_K_M quantized model. (temperature=0.7, repetition_penalty=1.1)</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Scientists have discovered a new planet that may be capable of supporting life!</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/4.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">The model partially failed to follow the input text. (temperature=0.1, repetition_penalty=1.1) </td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Scientists have discovered a new planet that may be capable of supporting life!</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/OuteAI/OuteTTS-0.1-350M/resolve/main/samples/5.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">In this case, changing to a higher temperature from 0.1 to 0.7 produces more consistent output. (temperature=0.7, repetition_penalty=1.1)</td>
+    </tr>
+  </tbody>
+  </table>
+</div>
+## Installation
+[![GitHub](https://img.shields.io/badge/GitHub-OuteTTS-181717?logo=github)](https://github.com/edwko/OuteTTS)
+```bash
+pip install outetts
+```
+## Usage
+> [!WARNING]
+> The example below works with older `outetts` version (`==0.1.7`). The new version (`>=0.2.0`) introduces changes to the interface. Please refer to the [GitHub Usage Example](https://github.com/edwko/OuteTTS?tab=readme-ov-file#usage) for updated examples.
+### Interface Usage
+```python
+from outetts.v0_1.interface import InterfaceHF, InterfaceGGUF
+# Initialize the interface with the Hugging Face model
+interface = InterfaceHF("OuteAI/OuteTTS-0.1-350M")
+# Or initialize the interface with a GGUF model
+# interface = InterfaceGGUF("path/to/model.gguf")
+# Generate TTS output
+# Without a speaker reference, the model generates speech with random speaker characteristics
+output = interface.generate(
+    text="Hello, am I working?",
+    temperature=0.1,
+    repetition_penalty=1.1,
+    max_length=4096
+)
+# Play the generated audio
+output.play()
+# Save the generated audio to a file
+output.save("output.wav")
+```
+### Voice Cloning
+```python
+# Create a custom speaker from an audio file
+speaker = interface.create_speaker(
+    "path/to/reference.wav",
+    "reference text matching the audio"
+)
+# Generate TTS with the custom voice
+output = interface.generate(
+    text="This is a cloned voice speaking",
+    speaker=speaker,
+    temperature=0.1,
+    repetition_penalty=1.1,
+    max_length=4096
+)
 ```
+## Model Details
+- **Model Type:** LLaMa-based language model
+- **Size:** 350M parameters
+- **Language Support:** English
+- **License:** CC BY 4.0
+- **Speech Datasets Used:**
+  - LibriTTS-R (CC BY 4.0)
+  - Multilingual LibriSpeech (MLS) (CC BY 4.0)
+## Future Improvements
+- Scaling up parameters and training data
+- Exploring alternative alignment methods for better character compatibility
+- Potential expansion into speech-to-speech assistant models
+## Credits
+- WavTokenizer: https://github.com/jishengpeng/WavTokenizer
+- CTC Forced Alignment: https://pytorch.org/audio/stable/tutorials/ctc_forced_alignment_api_tutorial.html
+## Disclaimer
+By using this model, you acknowledge that you understand and assume the risks associated with its use.
+You are solely responsible for ensuring compliance with all applicable laws and regulations.
+We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages.
+We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.

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