Instructions to use Defetya/jumoreski-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Defetya/jumoreski-clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Defetya/jumoreski-clean")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Defetya/jumoreski-clean") model = AutoModelForCausalLM.from_pretrained("Defetya/jumoreski-clean") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Defetya/jumoreski-clean with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Defetya/jumoreski-clean" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Defetya/jumoreski-clean", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Defetya/jumoreski-clean
- SGLang
How to use Defetya/jumoreski-clean with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Defetya/jumoreski-clean" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Defetya/jumoreski-clean", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Defetya/jumoreski-clean" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Defetya/jumoreski-clean", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Defetya/jumoreski-clean with Docker Model Runner:
docker model run hf.co/Defetya/jumoreski-clean
Upload model
#7
by Defetya - opened
- README.md +19 -1
- adapter_config.json +22 -0
- adapter_model.bin +3 -0
README.md
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---
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-
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library_name: peft
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- quant_method: bitsandbytes
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float16
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### Framework versions
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- PEFT 0.6.0.dev0
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "Defetya/sharded-mgpt",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"c_attn"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:89f1c900b31eff1247e87fd9cf096ddaefc952f5f63e6ac9cf165fadb5a91c89
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size 209743521
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