Text Generation
Safetensors
GGUF
PyTorch
English
picolm_v2
conversational
causal-lm
slm
on-device
mobilellm
layer-sharing
custom_code
Instructions to use aethertp/PicoLM-V2.1-81M-Instruct 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 aethertp/PicoLM-V2.1-81M-Instruct 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 aethertp/PicoLM-V2.1-81M-Instruct # Run inference directly in the terminal: llama cli -hf aethertp/PicoLM-V2.1-81M-Instruct
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aethertp/PicoLM-V2.1-81M-Instruct # Run inference directly in the terminal: llama cli -hf aethertp/PicoLM-V2.1-81M-Instruct
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 aethertp/PicoLM-V2.1-81M-Instruct # Run inference directly in the terminal: ./llama-cli -hf aethertp/PicoLM-V2.1-81M-Instruct
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 aethertp/PicoLM-V2.1-81M-Instruct # Run inference directly in the terminal: ./build/bin/llama-cli -hf aethertp/PicoLM-V2.1-81M-Instruct
Use Docker
docker model run hf.co/aethertp/PicoLM-V2.1-81M-Instruct
- LM Studio
- Jan
- vLLM
How to use aethertp/PicoLM-V2.1-81M-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aethertp/PicoLM-V2.1-81M-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aethertp/PicoLM-V2.1-81M-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aethertp/PicoLM-V2.1-81M-Instruct
- Ollama
How to use aethertp/PicoLM-V2.1-81M-Instruct with Ollama:
ollama run hf.co/aethertp/PicoLM-V2.1-81M-Instruct
- Unsloth Desktop
- Docker Model Runner
How to use aethertp/PicoLM-V2.1-81M-Instruct with Docker Model Runner:
docker model run hf.co/aethertp/PicoLM-V2.1-81M-Instruct
- Lemonade
How to use aethertp/PicoLM-V2.1-81M-Instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aethertp/PicoLM-V2.1-81M-Instruct
Run and chat with the model
lemonade run user.PicoLM-V2.1-81M-Instruct-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Drop redundant untied lm_head.weight (Option 1) The checkpoint stored both tok_embeddings.weight and a separate lm_head.weight, each [24576, 576] = 14,155,776 params, even though config.json sets tie_word_embeddings: true. At runtime the head is tied to the embedding, so lm_head.weight is never read. This removes that one tensor. Verified: - 201 -> 200 tensors; stored elements 96,017,472 -> 81,861,696 (exactly the card's count) - file 192,054,704 -> 163,743,061 bytes (-28.3 MB) - loaded both versions with the repo's own PicoLMV2ForCausalLM: identical logits on fixed inputs (max |diff| = 0.0), same 81,861,696 unique params, head tied to embedding in both. Requested by aethertp in the discussion (Option 1).
#2
by Compactbot - opened
No description provided.
aethertp changed pull request status to merged