Instructions to use mphd1/opt6.7b-dolly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mphd1/opt6.7b-dolly with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mphd1/opt6.7b-dolly")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mphd1/opt6.7b-dolly") model = AutoModelForCausalLM.from_pretrained("mphd1/opt6.7b-dolly", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mphd1/opt6.7b-dolly with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mphd1/opt6.7b-dolly" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mphd1/opt6.7b-dolly", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mphd1/opt6.7b-dolly
- SGLang
How to use mphd1/opt6.7b-dolly 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 "mphd1/opt6.7b-dolly" \ --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": "mphd1/opt6.7b-dolly", "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 "mphd1/opt6.7b-dolly" \ --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": "mphd1/opt6.7b-dolly", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mphd1/opt6.7b-dolly with Docker Model Runner:
docker model run hf.co/mphd1/opt6.7b-dolly
opt6.7b
This model is a fine-tuned version of facebook/opt-6.7b on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.6760
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.8948 | 1.0 | 538 | 1.8861 |
| 1.1137 | 2.0 | 1076 | 1.9893 |
| 0.5561 | 3.0 | 1614 | 2.2066 |
| 0.2579 | 4.0 | 2152 | 2.4634 |
| 0.1481 | 5.0 | 2690 | 2.6760 |
Framework versions
- Transformers 5.14.1
- Pytorch 2.13.0+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for mphd1/opt6.7b-dolly
Base model
facebook/opt-6.7b