Instructions to use nagrajn/TinyLinuxDSLM81M_EXT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nagrajn/TinyLinuxDSLM81M_EXT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nagrajn/TinyLinuxDSLM81M_EXT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nagrajn/TinyLinuxDSLM81M_EXT") model = AutoModelForCausalLM.from_pretrained("nagrajn/TinyLinuxDSLM81M_EXT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nagrajn/TinyLinuxDSLM81M_EXT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nagrajn/TinyLinuxDSLM81M_EXT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nagrajn/TinyLinuxDSLM81M_EXT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nagrajn/TinyLinuxDSLM81M_EXT
- SGLang
How to use nagrajn/TinyLinuxDSLM81M_EXT 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 "nagrajn/TinyLinuxDSLM81M_EXT" \ --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": "nagrajn/TinyLinuxDSLM81M_EXT", "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 "nagrajn/TinyLinuxDSLM81M_EXT" \ --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": "nagrajn/TinyLinuxDSLM81M_EXT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nagrajn/TinyLinuxDSLM81M_EXT with Docker Model Runner:
docker model run hf.co/nagrajn/TinyLinuxDSLM81M_EXT
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Download README.md from nagrajn/TinyLinuxDSLM81M_EXT: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/nagrajn/TinyLinuxDSLM81M_EXT/resolve/main/README.md
- Command line
-
hf download hf://nagrajn/TinyLinuxDSLM81M_EXT/README.md
-
curl -L -o README.md https://huggingface.co/nagrajn/TinyLinuxDSLM81M_EXT/resolve/main/README.md
1.33 kB
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| model-index: | |
| - name: TinyLinuxDSLM | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # TinyLinuxDSLM | |
| This model is a fine-tuned version of [](https://huggingface.co/) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5454 | |
| ## 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: 2e-05 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 32 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.609 | 1.0 | 3125 | 0.5880 | | |
| | 0.5796 | 2.0 | 6250 | 0.5454 | | |
| ### Framework versions | |
| - Transformers 4.41.2 | |
| - Pytorch 2.3.1+cu118 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |