Instructions to use TurkishCodeMan/csm-1b-lora-fft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TurkishCodeMan/csm-1b-lora-fft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="TurkishCodeMan/csm-1b-lora-fft")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TurkishCodeMan/csm-1b-lora-fft", dtype="auto") - PEFT
How to use TurkishCodeMan/csm-1b-lora-fft with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use TurkishCodeMan/csm-1b-lora-fft 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 TurkishCodeMan/csm-1b-lora-fft 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 TurkishCodeMan/csm-1b-lora-fft to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TurkishCodeMan/csm-1b-lora-fft to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="TurkishCodeMan/csm-1b-lora-fft", max_seq_length=2048, )
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- README.md +6 -6
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
README.md
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license: apache-2.0
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base_model: unsloth/csm-1b
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tags:
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library_name: transformers
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---
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license: apache-2.0
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base_model: unsloth/csm-1b
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tags:
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- unsloth
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- peft
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- lora
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- text-to-speech
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- speech
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- audio
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library_name: transformers
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---
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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