Instructions to use codllm/codllm-base-deploy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codllm/codllm-base-deploy with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="codllm/codllm-base-deploy")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("codllm/codllm-base-deploy")
model = AutoModelForSeq2SeqLM.from_pretrained("codllm/codllm-base-deploy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codllm/codllm-base-deploy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codllm/codllm-base-deploy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codllm/codllm-base-deploy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codllm/codllm-base-deploy

SGLang

How to use codllm/codllm-base-deploy 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 "codllm/codllm-base-deploy" \
    --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": "codllm/codllm-base-deploy",
		"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 "codllm/codllm-base-deploy" \
        --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": "codllm/codllm-base-deploy",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codllm/codllm-base-deploy with Docker Model Runner:
```
docker model run hf.co/codllm/codllm-base-deploy
```

lucasmortensen commited on 2 days ago

Commit

565d66a

verified ·

1 Parent(s): 5cff974

Update public access instructions and add COD widget examples

Browse files

Files changed (1) hide show

README.md +5 -2

README.md CHANGED Viewed

@@ -3,6 +3,11 @@ license: apache-2.0
 library_name: transformers
 pipeline_tag: text-generation
 base_model: google/flan-t5-base
 language:
   - en
   - nl
@@ -41,8 +46,6 @@ Install the inference dependencies; Transformers 5.1.0 is the version recorded w
 uv add "transformers==5.1.0" torch sentencepiece
 ```
-The repository is currently private. An account with access must first authenticate with `hf auth login`.
 ```python
 import torch
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

 library_name: transformers
 pipeline_tag: text-generation
 base_model: google/flan-t5-base
+widget:
+  - text: "cod: fever | age: unknown | sex: unknown"
+    example_title: "Cause text with missing metadata"
+  - text: "cod: pneumonia | age: 65 | sex: female"
+    example_title: "Cause text with age and sex"
 language:
   - en
   - nl
 uv add "transformers==5.1.0" torch sentencepiece
 ```
 ```python
 import torch
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer