Instructions to use modularai/replit-code-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use modularai/replit-code-1.5 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="modularai/replit-code-1.5", filename="replit-code-v1_5-3b-bf16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use modularai/replit-code-1.5 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 modularai/replit-code-1.5:BF16 # Run inference directly in the terminal: llama cli -hf modularai/replit-code-1.5:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf modularai/replit-code-1.5:BF16 # Run inference directly in the terminal: llama cli -hf modularai/replit-code-1.5:BF16
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 modularai/replit-code-1.5:BF16 # Run inference directly in the terminal: ./llama-cli -hf modularai/replit-code-1.5:BF16
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 modularai/replit-code-1.5:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf modularai/replit-code-1.5:BF16
Use Docker
docker model run hf.co/modularai/replit-code-1.5:BF16
- LM Studio
- Jan
- Ollama
How to use modularai/replit-code-1.5 with Ollama:
ollama run hf.co/modularai/replit-code-1.5:BF16
- Unsloth Studio
How to use modularai/replit-code-1.5 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 modularai/replit-code-1.5 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 modularai/replit-code-1.5 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for modularai/replit-code-1.5 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use modularai/replit-code-1.5 with Docker Model Runner:
docker model run hf.co/modularai/replit-code-1.5:BF16
- Lemonade
How to use modularai/replit-code-1.5 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull modularai/replit-code-1.5:BF16
Run and chat with the model
lemonade run user.replit-code-1.5-BF16
List all available models
lemonade list
File size: 1,274 Bytes
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"architectures": [
"MPTForCausalLM"
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"attn_config": {
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"attn_impl": "torch",
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"attn_type": "grouped_query_attention",
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"prefix_lm": false,
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"auto_map": {
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"AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"
},
"d_model": 3072,
"emb_pdrop": 0.0,
"embedding_fraction": 1.0,
"expansion_ratio": 4,
"fc_type": "torch",
"ffn_config": {
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"ffn_type": "mptmlp"
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"init_config": {
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"fan_mode": "fan_in",
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"model_type": "mpt",
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"n_layers": 32,
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"norm_type": "low_precision_layernorm",
"resid_pdrop": 0.0,
"torch_dtype": "bfloat16",
"transformers_version": "4.33.3",
"use_cache": false,
"vocab_size": 32768
}
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