Instructions to use ressurectAI/base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ressurectAI/base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ressurectAI/base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ressurectAI/base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ressurectAI/base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ressurectAI/base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ressurectAI/base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ressurectAI/base
- SGLang
How to use ressurectAI/base 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 "ressurectAI/base" \ --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": "ressurectAI/base", "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 "ressurectAI/base" \ --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": "ressurectAI/base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ressurectAI/base with Docker Model Runner:
docker model run hf.co/ressurectAI/base
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- README.md +2 -2
- adapter_config.json +34 -0
- adapter_model.safetensors +3 -0
README.md
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---
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license: apache-2.0
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pipeline_tag: text-generation
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datasets:
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- ressurectAI/gandhigiri
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language:
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- en
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library_name: transformers
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---
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Gandhigiri model that talks like gandhi ji, walk like gandhi
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---
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datasets:
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- ressurectAI/gandhigiri
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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---
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Gandhigiri model that talks like gandhi ji, walk like gandhi
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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": "unsloth/Meta-Llama-3.1-8B-bnb-4bit",
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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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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0,
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"megatron_config": null,
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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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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"o_proj",
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"gate_proj",
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"v_proj",
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"k_proj",
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"up_proj",
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"q_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": true
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c0173e37e4c2237272ccc1dc5375aa973947fe21c70366bd3541f4fa2d30c949
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size 167832240
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