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")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-500M")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-500M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceBio/Carbon-500M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Update model card namespace references

by lewtun HF Staff - opened 7 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-8

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -14,9 +14,9 @@ tags:
 A small generative DNA model from the **Carbon** family.
-**Carbon-500M is intended primarily as a draft model for speculative decoding** — it shares the tokenizer and DNA template format of [Carbon-3B](https://huggingface.co/hf-carbon/Carbon-3B) and [Carbon-8B](https://huggingface.co/hf-carbon/Carbon-8B), so it can be paired with either as the target model to reduce wall-clock generation cost at no quality loss. It is not designed to be competitive with the 3B/8B Carbon models on downstream benchmarks.
-For the full design rationale, tokenizer specification, evaluation protocol, and usage notes (DNA tag wrapping, 6-mer constraints, scoring helpers), please refer to the **[Carbon-3B model card](https://huggingface.co/hf-carbon/Carbon-3B)** — this card focuses only on facts specific to Carbon-500M.
 > TODO: update teh tokenizer code
@@ -34,13 +34,13 @@ For the full design rationale, tokenizer specification, evaluation protocol, and
 ## How to use
-Wrap DNA in `<dna>...</dna>` exactly as for the larger models. See the [Carbon-3B card](https://huggingface.co/hf-carbon/Carbon-3B#tokenizer-working-with-dna-inputs) for tokenizer details.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-repo = "hf-carbon/Carbon-500M"
 tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     repo, torch_dtype=torch.bfloat16,
@@ -60,12 +60,12 @@ Carbon-500M is most useful when paired with a larger Carbon model as the verifie
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-tok    = AutoTokenizer.from_pretrained("hf-carbon/Carbon-3B", trust_remote_code=True)
 draft  = AutoModelForCausalLM.from_pretrained(
-    "hf-carbon/Carbon-500M", torch_dtype=torch.bfloat16
 ).cuda().eval()
 target = AutoModelForCausalLM.from_pretrained(
-    "hf-carbon/Carbon-3B",   torch_dtype=torch.bfloat16
 ).cuda().eval()
 prompt = "<dna>ATGCGCTAGCTACGATCGATCGTAGCTAGCTAGCTAGCTACG"
@@ -81,7 +81,7 @@ Output is guaranteed identical to greedy decoding with the target model alone; o
 ## Evaluation
-Carbon-500M is benchmarked against  ≈ 1B-parameter DNA models on the standard Carbon evaluation suite. See the [Carbon-3B card](https://huggingface.co/hf-carbon/Carbon-3B#evaluation) for the task definitions and methodology.
 > TODO Loubna: add one downstream table comparing Carbon-500M to other 1B-class baselines. -->

 A small generative DNA model from the **Carbon** family.
+**Carbon-500M is intended primarily as a draft model for speculative decoding** — it shares the tokenizer and DNA template format of [Carbon-3B](https://huggingface.co/HuggingFaceBio/Carbon-3B) and [Carbon-8B](https://huggingface.co/HuggingFaceBio/Carbon-8B), so it can be paired with either as the target model to reduce wall-clock generation cost at no quality loss. It is not designed to be competitive with the 3B/8B Carbon models on downstream benchmarks.
+For the full design rationale, tokenizer specification, evaluation protocol, and usage notes (DNA tag wrapping, 6-mer constraints, scoring helpers), please refer to the **[Carbon-3B model card](https://huggingface.co/HuggingFaceBio/Carbon-3B)** — this card focuses only on facts specific to Carbon-500M.
 > TODO: update teh tokenizer code
 ## How to use
+Wrap DNA in `<dna>...</dna>` exactly as for the larger models. See the [Carbon-3B card](https://huggingface.co/HuggingFaceBio/Carbon-3B#tokenizer-working-with-dna-inputs) for tokenizer details.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
+repo = "HuggingFaceBio/Carbon-500M"
 tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     repo, torch_dtype=torch.bfloat16,
 from transformers import AutoModelForCausalLM, AutoTokenizer
 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"
 ## Evaluation
+Carbon-500M is benchmarked against  ≈ 1B-parameter DNA models on the standard Carbon evaluation suite. See the [Carbon-3B card](https://huggingface.co/HuggingFaceBio/Carbon-3B#evaluation) for the task definitions and methodology.
 > TODO Loubna: add one downstream table comparing Carbon-500M to other 1B-class baselines. -->