Text Generation
Transformers
Safetensors
GGUF
English
Turkish
llama
asena
bce
esp32
edge
esp32s3
microllm
chat
text-generation-inference
agent
prettybird
consciousness
conscious
llm
optimized
ethic
secure
turkish
english
behavioral-consciousness-engine
model
instruct
iot
LittleFS
SPIFFS
reasoning
thinking
think
god edge ai
extreme edge ai
cicikus
cicikuş
embedded
robot
npc
Offline assistant
guard
pre filter
tiny-llm
tiny llm
Eval Results (legacy)
Instructions to use pthinc/Asena_ESP32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Upload folder using huggingface_hub
Browse files- config.json +38 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +9 -0
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.00271828182,
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"bos_token_id": 8001,
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"dtype": "float32",
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"eos_token_id": 8002,
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"head_dim": 26,
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"hidden_act": "silu",
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"hidden_dropout": 0.00271828182,
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"hidden_size": 64,
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"initializer_range": 0.0215816655,
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"intermediate_size": 208,
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"max_position_embeddings": 128,
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"mlp_bias": true,
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"model_type": "llama",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"num_key_value_heads": 4,
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"pad_token_id": 8000,
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"pretraining_tp": 1,
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"rms_norm_eps": 1.168034e-05,
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"rope_parameters": {
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"factor": 256.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 16,
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"rope_theta": 12566.37061436,
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"rope_type": "llama3",
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"type": "llama3"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"vocab_size": 8766
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 8001,
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"eos_token_id": 8002,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 8000,
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"transformers_version": "5.0.0",
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"use_cache": true
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:91d4c1e66e32b923da5e69b4a3b7f4baf626ae12c632398681a06663f537d5f4
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size 4848504
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "[BOS]",
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"eos_token": "[EOS]",
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "[UNK]"
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}
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