Instructions to use replit/replit-code-v1-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use replit/replit-code-v1-3b with Transformers:

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

pipe = pipeline("text-generation", model="replit/replit-code-v1-3b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("replit/replit-code-v1-3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("replit/replit-code-v1-3b", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use replit/replit-code-v1-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "replit/replit-code-v1-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "replit/replit-code-v1-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/replit/replit-code-v1-3b

SGLang

How to use replit/replit-code-v1-3b 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 "replit/replit-code-v1-3b" \
    --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": "replit/replit-code-v1-3b",
		"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 "replit/replit-code-v1-3b" \
        --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": "replit/replit-code-v1-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use replit/replit-code-v1-3b with Docker Model Runner:
```
docker model run hf.co/replit/replit-code-v1-3b
```

dobbySeo commited on Apr 17, 2024

Commit

c69cea3

verified ·

1 Parent(s): cc0a4f1

Update replit_lm_tokenizer.py

Browse files

When initializing the tokenizer, error occurs that using sp_model before sp_model instance initialization.

```
Traceback (most recent call last):
File "/home/dobby/trainer/examples/function_calling/preprocessing.py", line 93, in <module>
preprocess(bpe_process, args)
File "/home/dobby/trainer/examples/function_calling/preprocessing.py", line 53, in preprocess
tokenizer = AutoTokenizer.from_pretrained(
File "/home/dobby/.cache/pypoetry/virtualenvs/trainer-lQlppuIx-py3.10/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 819, in from_pretrained
return tokenizer_class.from_pretrained(
File "/home/dobby/.cache/pypoetry/virtualenvs/trainer-lQlppuIx-py3.10/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2059, in from_pretrained
return cls._from_pretrained(
File "/home/dobby/.cache/pypoetry/virtualenvs/trainer-lQlppuIx-py3.10/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2298, in _from_pretrained
tokenizer = cls(*init_inputs, **init_kwargs)
File "/home/dobby/.cache/huggingface/modules/transformers_modules/replit/replit-code-v1-3b/cc0a4f17a8d72b71d62ea53cb0e23e4dac352067/replit_lm_tokenizer.py", line 66, in __init__
super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
File "/home/dobby/.cache/pypoetry/virtualenvs/trainer-lQlppuIx-py3.10/lib/python3.10/site-packages/transformers/tokenization_utils.py", line 367, in __init__
self._add_tokens(
File "/home/dobby/.cache/pypoetry/virtualenvs/trainer-lQlppuIx-py3.10/lib/python3.10/site-packages/transformers/tokenization_utils.py", line 467, in _add_tokens
current_vocab = self.get_vocab().copy()
File "/home/dobby/.cache/huggingface/modules/transformers_modules/replit/replit-code-v1-3b/cc0a4f17a8d72b71d62ea53cb0e23e4dac352067/replit_lm_tokenizer.py", line 76, in get_vocab
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
File "/home/dobby/.cache/huggingface/modules/transformers_modules/replit/replit-code-v1-3b/cc0a4f17a8d72b71d62ea53cb0e23e4dac352067/replit_lm_tokenizer.py", line 73, in vocab_size
return self.sp_model.get_piece_size()
AttributeError: 'ReplitLMTokenizer' object has no attribute 'sp_model'
```

So I change super class initialization at the last.

Files changed (1) hide show

replit_lm_tokenizer.py +2 -1

replit_lm_tokenizer.py CHANGED Viewed

@@ -63,10 +63,11 @@ class ReplitLMTokenizer(PreTrainedTokenizer):
     def __init__(self, vocab_file, bos_token=None, eos_token='<|endoftext|>', unk_token='<|unk|>', pad_token='<|pad|>', sep_token=None, sp_model_kwargs: Optional[Dict[str, Any]]=None, **kwargs) -> None:
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
-        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
         self.vocab_file = vocab_file
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(vocab_file)
     @property
     def vocab_size(self):

     def __init__(self, vocab_file, bos_token=None, eos_token='<|endoftext|>', unk_token='<|unk|>', pad_token='<|pad|>', sep_token=None, sp_model_kwargs: Optional[Dict[str, Any]]=None, **kwargs) -> None:
         self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        # super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
         self.vocab_file = vocab_file
         self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(vocab_file)
+        super().__init__(bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs)
     @property
     def vocab_size(self):