Instructions to use Minata/codegen-350M-finetuned_method2test_ctx11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minata/codegen-350M-finetuned_method2test_ctx11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Minata/codegen-350M-finetuned_method2test_ctx11")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Minata/codegen-350M-finetuned_method2test_ctx11") model = AutoModelForCausalLM.from_pretrained("Minata/codegen-350M-finetuned_method2test_ctx11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Minata/codegen-350M-finetuned_method2test_ctx11 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Minata/codegen-350M-finetuned_method2test_ctx11" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minata/codegen-350M-finetuned_method2test_ctx11", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Minata/codegen-350M-finetuned_method2test_ctx11
- SGLang
How to use Minata/codegen-350M-finetuned_method2test_ctx11 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 "Minata/codegen-350M-finetuned_method2test_ctx11" \ --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": "Minata/codegen-350M-finetuned_method2test_ctx11", "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 "Minata/codegen-350M-finetuned_method2test_ctx11" \ --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": "Minata/codegen-350M-finetuned_method2test_ctx11", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Minata/codegen-350M-finetuned_method2test_ctx11 with Docker Model Runner:
docker model run hf.co/Minata/codegen-350M-finetuned_method2test_ctx11
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.32bb8bb
-
Training in progress, step 43500
47284f3 -
Training in progress, step 42000
c0de8e9 -
Training in progress, step 40500
78b8172 -
Training in progress, step 39000
ac00a91 -
Training in progress, step 37000
18aef6a -
Training in progress, step 35000
8c69e21 -
Training in progress, step 33500
880a651 -
Training in progress, step 32000
7dba624 -
Training in progress, step 30500
0cc9e13 -
Training in progress, step 29000
e8bc094 -
Training in progress, step 27500
5207b6d -
Training in progress, step 26000
9beff2a -
Training in progress, step 24500
5d686d0 -
Training in progress, step 23000
9d3c414 -
Training in progress, step 22000
8230761 -
Training in progress, step 20500
9150dbe -
Training in progress, step 19000
73abb8c -
Training in progress, step 17500
4acf2ad -
Training in progress, step 16000
3b8f79c -
Training in progress, step 14500
9e4705c -
Training in progress, step 12500
c22623f -
Training in progress, step 11000
f918608 -
Training in progress, step 9500
4aa1202 -
Training in progress, step 8000
bda2438 -
Training in progress, step 6500
c2d181d -
Training in progress, step 5000
dbadb9d -
Training in progress, step 3500
1844d13 -
Training in progress, step 2000
4d7c4b6 -
Training in progress, step 500
3afccad -
initial commit
0023c0c