Instructions to use HuggingFaceBio/Carbon-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceBio/Carbon-500M with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceBio/Carbon-500M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-500M")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-500M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceBio/Carbon-500M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceBio/Carbon-500M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-500M

SGLang

How to use HuggingFaceBio/Carbon-500M 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 "HuggingFaceBio/Carbon-500M" \
    --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": "HuggingFaceBio/Carbon-500M",
		"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 "HuggingFaceBio/Carbon-500M" \
        --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": "HuggingFaceBio/Carbon-500M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceBio/Carbon-500M with Docker Model Runner:
```
docker model run hf.co/HuggingFaceBio/Carbon-500M
```

kashif HF Staff commited on 7 days ago

Commit

c933421

verified ·

1 Parent(s): 9d803d5

docs: fix dtype, remove trust_remote_code for model, clean up internal comments

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -41,7 +41,7 @@ import torch
 repo = "HuggingFaceBio/Carbon-500M"
 tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
-    repo, torch_dtype=torch.bfloat16,
 ).cuda().eval()
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"
@@ -60,10 +60,10 @@ import torch
 tok    = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-3B", trust_remote_code=True)
 draft  = AutoModelForCausalLM.from_pretrained(
-    "HuggingFaceBio/Carbon-500M", torch_dtype=torch.bfloat16
 ).cuda().eval()
 target = AutoModelForCausalLM.from_pretrained(
-    "HuggingFaceBio/Carbon-3B",   torch_dtype=torch.bfloat16
 ).cuda().eval()
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"

 repo = "HuggingFaceBio/Carbon-500M"
 tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
+    repo, dtype=torch.bfloat16,
 ).cuda().eval()
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"
 tok    = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-3B", trust_remote_code=True)
 draft  = AutoModelForCausalLM.from_pretrained(
+    "HuggingFaceBio/Carbon-500M", dtype=torch.bfloat16
 ).cuda().eval()
 target = AutoModelForCausalLM.from_pretrained(
+    "HuggingFaceBio/Carbon-3B",   dtype=torch.bfloat16
 ).cuda().eval()
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"