Instructions to use LnL-AI/dbrx-base-converted-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LnL-AI/dbrx-base-converted-v2 with Transformers:

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

pipe = pipeline("text-generation", model="LnL-AI/dbrx-base-converted-v2", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LnL-AI/dbrx-base-converted-v2", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("LnL-AI/dbrx-base-converted-v2", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LnL-AI/dbrx-base-converted-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LnL-AI/dbrx-base-converted-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LnL-AI/dbrx-base-converted-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LnL-AI/dbrx-base-converted-v2

SGLang

How to use LnL-AI/dbrx-base-converted-v2 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 "LnL-AI/dbrx-base-converted-v2" \
    --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": "LnL-AI/dbrx-base-converted-v2",
		"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 "LnL-AI/dbrx-base-converted-v2" \
        --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": "LnL-AI/dbrx-base-converted-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LnL-AI/dbrx-base-converted-v2 with Docker Model Runner:
```
docker model run hf.co/LnL-AI/dbrx-base-converted-v2
```

Update tiktoken.py

by Qubitium - opened Apr 3, 2024

base: refs/heads/main

←

from: refs/pr/4

Discussion Files changed

-2

Files changed (1) hide show

tiktoken.py +2 -2

tiktoken.py CHANGED Viewed

@@ -247,7 +247,7 @@ class TiktokenTokenizerWrapper(PreTrainedTokenizer):
             # Get an index to add and add the item
             vocab_clone[candidate_extra_id] = index_to_add
-        return vocab_clone
     def _tokenize(self, text: str) -> List[str]:
         """Returns a tokenized string."""
@@ -371,4 +371,4 @@ class TiktokenTokenizerWrapper(PreTrainedTokenizer):
             if len(encoded) > 1:
                 actual_new_tokens.append(token)
-        return self.add_tokens(actual_new_tokens, special_tokens=True)

             # Get an index to add and add the item
             vocab_clone[candidate_extra_id] = index_to_add
+        return dict(vocab_clone, **self.added_tokens_encoder)
     def _tokenize(self, text: str) -> List[str]:
         """Returns a tokenized string."""
             if len(encoded) > 1:
                 actual_new_tokens.append(token)
+        return self.add_tokens(actual_new_tokens, special_tokens=True)