Instructions to use tampakwill/AWA-3M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tampakwill/AWA-3M-Base with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tampakwill/AWA-3M-Base # Run inference directly in the terminal: llama cli -hf tampakwill/AWA-3M-Base
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tampakwill/AWA-3M-Base # Run inference directly in the terminal: llama cli -hf tampakwill/AWA-3M-Base
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf tampakwill/AWA-3M-Base # Run inference directly in the terminal: ./build/bin/llama cli -hf tampakwill/AWA-3M-Base
Use Docker
docker model run hf.co/tampakwill/AWA-3M-Base
- Llama new
- LM Studio
- Jan
- Ollama
How to use tampakwill/AWA-3M-Base with Ollama:
ollama run hf.co/tampakwill/AWA-3M-Base
- Unsloth Desktop
- Docker Model Runner
How to use tampakwill/AWA-3M-Base with Docker Model Runner:
docker model run hf.co/tampakwill/AWA-3M-Base
- Lemonade
How to use tampakwill/AWA-3M-Base with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tampakwill/AWA-3M-Base
Run and chat with the model
lemonade run user.AWA-3M-Base-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download config.json from tampakwill/AWA-3M-Base: direct link, hf CLI and curl.
- Browser
- Download file 835 Bytes
-
https://huggingface.co/tampakwill/AWA-3M-Base/resolve/main/config.json
- Command line
-
hf download hf://tampakwill/AWA-3M-Base/config.json
-
curl -L -o config.json https://huggingface.co/tampakwill/AWA-3M-Base/resolve/main/config.json
835 Bytes
| { | |
| "activation_function": "gelu_new", | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "GPT2LMHeadModel" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 4, | |
| "dtype": "float32", | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 5, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "gpt2", | |
| "n_ctx": 1024, | |
| "n_embd": 176, | |
| "n_head": 4, | |
| "n_inner": null, | |
| "n_layer": 8, | |
| "n_positions": 1024, | |
| "pad_token_id": 5, | |
| "reorder_and_upcast_attn": false, | |
| "resid_pdrop": 0.1, | |
| "scale_attn_by_inverse_layer_idx": false, | |
| "scale_attn_weights": true, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "use_cache": false, | |
| "vocab_size": 1000 | |
| } | |