Text Generation
Transformers
PyTorch
TensorBoard
llama
Generated from Trainer
text-generation-inference
Instructions to use simlamkr1/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simlamkr1/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="simlamkr1/output")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("simlamkr1/output") model = AutoModelForCausalLM.from_pretrained("simlamkr1/output") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use simlamkr1/output with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "simlamkr1/output" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "simlamkr1/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/simlamkr1/output
- SGLang
How to use simlamkr1/output 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 "simlamkr1/output" \ --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": "simlamkr1/output", "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 "simlamkr1/output" \ --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": "simlamkr1/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use simlamkr1/output with Docker Model Runner:
docker model run hf.co/simlamkr1/output
End of training
Browse files
README.md
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model-index:
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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model-index:
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- name: output
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results: []
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library_name: peft
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Training procedure
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The following `bitsandbytes` quantization config was used during training:
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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: True
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- bnb_4bit_compute_dtype: float16
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### Training hyperparameters
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The following hyperparameters were used during training:
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### Framework versions
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- PEFT 0.4.0
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.5
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runs/Sep24_16-56-05_35f86f63965d/events.out.tfevents.1695574577.35f86f63965d.4470.3
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