Instructions to use nelson-pawait/checkpoints_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nelson-pawait/checkpoints_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nelson-pawait/checkpoints_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("nelson-pawait/checkpoints_3") model = AutoModelForCausalLM.from_pretrained("nelson-pawait/checkpoints_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nelson-pawait/checkpoints_3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nelson-pawait/checkpoints_3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nelson-pawait/checkpoints_3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nelson-pawait/checkpoints_3
- SGLang
How to use nelson-pawait/checkpoints_3 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 "nelson-pawait/checkpoints_3" \ --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": "nelson-pawait/checkpoints_3", "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 "nelson-pawait/checkpoints_3" \ --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": "nelson-pawait/checkpoints_3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nelson-pawait/checkpoints_3 with Docker Model Runner:
docker model run hf.co/nelson-pawait/checkpoints_3
Training in progress, step 3000
Browse files
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1826635576
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:901a665d4ff83e86ad57d9d878b15b3a240dbefebe43c59950d276b6e3bc74e7
|
| 3 |
size 1826635576
|
runs/May04_08-53-30_ccai-deeplearning-vm/events.out.tfevents.1714812865.ccai-deeplearning-vm.1535325.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:adb454da76c34e8de87a52d4d0aff677e1e70c6ff18efc6ae7d8ac33d6cdf77c
|
| 3 |
+
size 8729
|