Instructions to use pthinc/Asena_ESP32_MAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/Asena_ESP32_MAX with Transformers:

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

pipe = pipeline("text-generation", model="pthinc/Asena_ESP32_MAX")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pthinc/Asena_ESP32_MAX")
model = AutoModelForCausalLM.from_pretrained("pthinc/Asena_ESP32_MAX")

llama-cpp-python

How to use pthinc/Asena_ESP32_MAX with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="pthinc/Asena_ESP32_MAX",
	filename="gguf/asena_esp32max_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_MAX 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_MAX:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/Asena_ESP32_MAX:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf pthinc/Asena_ESP32_MAX:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf pthinc/Asena_ESP32_MAX:Q4_K_M

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_MAX:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf pthinc/Asena_ESP32_MAX:Q4_K_M

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_MAX:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pthinc/Asena_ESP32_MAX:Q4_K_M

Use Docker

docker model run hf.co/pthinc/Asena_ESP32_MAX:Q4_K_M

LM Studio
Jan

vLLM

How to use pthinc/Asena_ESP32_MAX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pthinc/Asena_ESP32_MAX"
# 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_MAX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pthinc/Asena_ESP32_MAX:Q4_K_M

SGLang

How to use pthinc/Asena_ESP32_MAX 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_MAX" \
    --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_MAX",
		"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_MAX" \
        --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_MAX",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pthinc/Asena_ESP32_MAX with Ollama:
```
ollama run hf.co/pthinc/Asena_ESP32_MAX:Q4_K_M
```

Unsloth Studio new

How to use pthinc/Asena_ESP32_MAX 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_MAX 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_MAX 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_MAX to start chatting

Docker Model Runner
How to use pthinc/Asena_ESP32_MAX with Docker Model Runner:
```
docker model run hf.co/pthinc/Asena_ESP32_MAX:Q4_K_M
```

Lemonade

How to use pthinc/Asena_ESP32_MAX with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/Asena_ESP32_MAX:Q4_K_M

Run and chat with the model

lemonade run user.Asena_ESP32_MAX-Q4_K_M

List all available models

lemonade list

Asena_ESP32_MAX

File size: 934 Bytes

445b09a

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  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": true,
  "attention_dropout": 0.00662607015,
  "bos_token_id": 8001,
  "dtype": "float32",
  "eos_token_id": 8002,
  "head_dim": 40,
  "hidden_act": "silu",
  "hidden_dropout": 0.00662607015,
  "hidden_size": 320,
  "initializer_range": 0.0215816655,
  "intermediate_size": 896,
  "max_position_embeddings": 1024,
  "mlp_bias": true,
  "model_type": "llama",
  "num_attention_heads": 8,
  "num_hidden_layers": 8,
  "num_key_value_heads": 4,
  "pad_token_id": 8000,
  "pretraining_tp": 1,
  "rms_norm_eps": 1.168034e-05,
  "rope_parameters": {
    "factor": 128.0,
    "high_freq_factor": 4.0,
    "low_freq_factor": 1.0,
    "original_max_position_embeddings": 128,
    "rope_theta": 12566.37061436,
    "rope_type": "llama3",
    "type": "llama3"
  },
  "tie_word_embeddings": true,
  "transformers_version": "5.0.0",
  "use_cache": false,
  "vocab_size": 8766
}