Instructions to use prajjwal1/ctrl_discovery_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/ctrl_discovery_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prajjwal1/ctrl_discovery_1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prajjwal1/ctrl_discovery_1") model = AutoModelForCausalLM.from_pretrained("prajjwal1/ctrl_discovery_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use prajjwal1/ctrl_discovery_1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prajjwal1/ctrl_discovery_1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prajjwal1/ctrl_discovery_1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/prajjwal1/ctrl_discovery_1
- SGLang
How to use prajjwal1/ctrl_discovery_1 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 "prajjwal1/ctrl_discovery_1" \ --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": "prajjwal1/ctrl_discovery_1", "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 "prajjwal1/ctrl_discovery_1" \ --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": "prajjwal1/ctrl_discovery_1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use prajjwal1/ctrl_discovery_1 with Docker Model Runner:
docker model run hf.co/prajjwal1/ctrl_discovery_1
prajjwal1 commited on
Commit ·
6cf8ada
1
Parent(s): 31b37ee
v1.1
Browse files- config.json +1 -1
- pytorch_model.bin +2 -2
- training_args.bin +1 -1
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "
|
| 3 |
"architectures": [
|
| 4 |
"CTRLLMHeadModel"
|
| 5 |
],
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "prajjwal1/ctrl_discovery_1",
|
| 3 |
"architectures": [
|
| 4 |
"CTRLLMHeadModel"
|
| 5 |
],
|
pytorch_model.bin
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 sha256:11d0feadbeb0a4eddab87d0cf6248171274facc396fea1ee7484e996ee1d60d3
|
| 3 |
+
size 6552165490
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2159
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8b3e849d38c5904ecc8297c26982a69f1b786b7951b89987bd640b72241d4997
|
| 3 |
size 2159
|