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 Settings
- Unsloth Studio
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
- adapter_config.json +8 -4
- adapter_model.safetensors +1 -1
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": {
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"base_model_class": "CsmForConditionalGeneration",
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"parent_library": "transformers.models.csm.modeling_csm",
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"base_model_name_or_path": "unsloth/csm-1b",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"qalora_group_size": 16,
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"r": 32,
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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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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": {
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"base_model_class": "CsmForConditionalGeneration",
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"parent_library": "transformers.models.csm.modeling_csm",
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"base_model_name_or_path": "unsloth/csm-1b",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.18.0",
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"qalora_group_size": 16,
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"o_proj",
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"k_proj",
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],
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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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