Instructions to use rparkr/LFM2.5-1.2B-Instruct-Coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rparkr/LFM2.5-1.2B-Instruct-Coding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rparkr/LFM2.5-1.2B-Instruct-Coding", dtype="auto") - PEFT
How to use rparkr/LFM2.5-1.2B-Instruct-Coding with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
chore: Add Ollama Modelfile
Browse files# Summary
Add an Ollama Modelfile demonstrating how to run this fine-tuned model locally using Ollama.
Modelfile
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# Ollama Modelfile to use this fine-tuned model with Ollama.
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# TODO (Ryan Parker, 7-May-2026): this Modelfile currently doesn't work.
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# When using safetensors, the base model must be downloaded first, and
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# Ollama raises an error stating that MLX is required for quantization.
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# Note that the base model can be downloaded like this:
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# uv tool run --from huggingface-hub -- hf download LiquidAI/LFM2.5-1.2B-Instruct
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# When using GGUF, Ollama successfully creates the model, but then raises
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# an error that LoRA is not supported.
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# safetensors version
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# Uses the adapter_model.safetensors trained in this repository.
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# Usage:
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# ollama create lfm-coder --file Modelfile --quantize q4_K_M --experimental
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# FROM hf.co/LiquidAI/LFM2.5-1.2B-Instruct
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# ADAPTER adapter_model.safetensors
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# GGUF version
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# Usage:
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# ollama create lfm-coder --file Modelfile
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FROM hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
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ADAPTER hf.co/rparkr/LFM2.5-1.2B-Instruct-Coding-F16-GGUF
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# Recommended settings from: https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct#:~:text=Generation%20parameters%3A
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PARAMETER temperature 0.1
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PARAMETER top_k 50
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PARAMETER repeat_penalty 1.05
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PARAMETER num_ctx 128000
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