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

Libraries

How to use HuggingFaceBio/Carbon-X-Base with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceBio/Carbon-X-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceBio/Carbon-X-Base")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceBio/Carbon-X-Base", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceBio/Carbon-X-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceBio/Carbon-X-Base"
# 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-X-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HuggingFaceBio/Carbon-X-Base

SGLang

How to use HuggingFaceBio/Carbon-X-Base 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-X-Base" \
    --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-X-Base",
		"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-X-Base" \
        --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-X-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Fix EOS override persistence on save and reload

by edbeeching HF Staff - opened 12 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

Files changed (1) hide show

tokenizer.py +4 -0

tokenizer.py CHANGED Viewed

@@ -716,6 +716,7 @@ class HybridDNATokenizer(PreTrainedTokenizer):
         # Save base tokenizer files with the wrapper's effective chat template.
         self._base_tokenizer.chat_template = self.chat_template
         self._base_tokenizer.save_pretrained(save_directory)
         # Save DNA config
@@ -816,6 +817,9 @@ class HybridDNATokenizer(PreTrainedTokenizer):
                 tokenizer_config = json.load(_f)
             if tokenizer_config.get("chat_template"):
                 _init["chat_template"] = tokenizer_config["chat_template"]
         chat_template_path = cached_file(pretrained_model_name_or_path, "chat_template.jinja", **hub_kwargs)
         if chat_template_path is not None:

         # Save base tokenizer files with the wrapper's effective chat template.
         self._base_tokenizer.chat_template = self.chat_template
+        self._base_tokenizer.eos_token = self.eos_token
         self._base_tokenizer.save_pretrained(save_directory)
         # Save DNA config
                 tokenizer_config = json.load(_f)
             if tokenizer_config.get("chat_template"):
                 _init["chat_template"] = tokenizer_config["chat_template"]
+            if tokenizer_config.get("eos_token") is not None:
+                eos_token = tokenizer_config["eos_token"]
+                _init["eos_token"] = eos_token["content"] if isinstance(eos_token, dict) else eos_token
         chat_template_path = cached_file(pretrained_model_name_or_path, "chat_template.jinja", **hub_kwargs)
         if chat_template_path is not None: