Instructions to use pthinc/Asena_ESP32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/Asena_ESP32 with Transformers:

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

pipe = pipeline("text-generation", model="pthinc/Asena_ESP32")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pthinc/Asena_ESP32")
model = AutoModelForCausalLM.from_pretrained("pthinc/Asena_ESP32")

llama-cpp-python

How to use pthinc/Asena_ESP32 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/Asena_ESP32",
	filename="gguf/asena_esp32_f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use pthinc/Asena_ESP32 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pthinc/Asena_ESP32:F16
# Run inference directly in the terminal:
llama-cli -hf pthinc/Asena_ESP32:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pthinc/Asena_ESP32:F16
# Run inference directly in the terminal:
llama-cli -hf pthinc/Asena_ESP32:F16

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 pthinc/Asena_ESP32:F16
# Run inference directly in the terminal:
./llama-cli -hf pthinc/Asena_ESP32:F16

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 pthinc/Asena_ESP32:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/Asena_ESP32:F16

Use Docker

docker model run hf.co/pthinc/Asena_ESP32:F16

LM Studio
Jan

vLLM

How to use pthinc/Asena_ESP32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pthinc/Asena_ESP32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/Asena_ESP32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pthinc/Asena_ESP32:F16

SGLang

How to use pthinc/Asena_ESP32 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 "pthinc/Asena_ESP32" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/Asena_ESP32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "pthinc/Asena_ESP32" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/Asena_ESP32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pthinc/Asena_ESP32 with Ollama:
```
ollama run hf.co/pthinc/Asena_ESP32:F16
```

Unsloth Studio new

How to use pthinc/Asena_ESP32 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 pthinc/Asena_ESP32 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 pthinc/Asena_ESP32 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for pthinc/Asena_ESP32 to start chatting

Docker Model Runner
How to use pthinc/Asena_ESP32 with Docker Model Runner:
```
docker model run hf.co/pthinc/Asena_ESP32:F16
```

Lemonade

How to use pthinc/Asena_ESP32 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/Asena_ESP32:F16

Run and chat with the model

lemonade run user.Asena_ESP32-F16

List all available models

lemonade list

prometechinc commited on 21 days ago

Commit

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1 Parent(s): 1fbdcfe

Update README.md

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@@ -103,6 +103,13 @@ This is not a large-scale knowledge model. Asena_ESP32 does not have deep expert
 **Practical Guidance:**
 For best results, keep prompts short, clear, and structured. Use domain-specific fine-tuning if you require higher accuracy in a particular field. Treat the model as a fast, efficient language generator rather than a comprehensive knowledge base. When used within its design limits, Asena_ESP32 can provide strong performance relative to its size in extreme edge AI scenarios.
 ---
 The **Asena_ESP32** model is built upon the **Behavioral Consciousness Engine (BCE)** architecture. Unlike traditional LLM datasets that focus solely on output accuracy, this dataset treats every response as a "behavioral journey" through the following mathematical frameworks:

 **Practical Guidance:**
 For best results, keep prompts short, clear, and structured. Use domain-specific fine-tuning if you require higher accuracy in a particular field. Treat the model as a fast, efficient language generator rather than a comprehensive knowledge base. When used within its design limits, Asena_ESP32 can provide strong performance relative to its size in extreme edge AI scenarios.
+### The most suitable use cases:
+- IoT device communication
+- Robot / embedded system command interpretation
+- Game NPC dialogue
+- Offline assistant (simple)
+- Guard / pre-filter model
 ---
 The **Asena_ESP32** model is built upon the **Behavioral Consciousness Engine (BCE)** architecture. Unlike traditional LLM datasets that focus solely on output accuracy, this dataset treats every response as a "behavioral journey" through the following mathematical frameworks: