Instructions to use euclaise/ReMask-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use euclaise/ReMask-3B with Transformers:

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

pipe = pipeline("text-generation", model="euclaise/ReMask-3B")
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("euclaise/ReMask-3B")
model = AutoModelForCausalLM.from_pretrained("euclaise/ReMask-3B", 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 euclaise/ReMask-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "euclaise/ReMask-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "euclaise/ReMask-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/euclaise/ReMask-3B

SGLang

How to use euclaise/ReMask-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 "euclaise/ReMask-3B" \
    --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": "euclaise/ReMask-3B",
		"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 "euclaise/ReMask-3B" \
        --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": "euclaise/ReMask-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use euclaise/ReMask-3B with Docker Model Runner:
```
docker model run hf.co/euclaise/ReMask-3B
```

euclaise commited on Mar 28, 2024

Commit

bfcd1ce

verified ·

1 Parent(s): 7b1caad

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -2

README.md CHANGED Viewed

@@ -33,8 +33,6 @@ In particular, it might be easy to predict a *reasonable* next token, but much m
 The correct prediction here might be "signs of life.". However, the model might predict "and" rather than "signs", since "and" is *reasonable* in the immediate context - it's gramatically correct, but implies a strange ending to the sentence.
 As a result, the model might end up with something like "The astronomer pointed his telescope at the distant star, hoping to see and hear." - which makes little sense.
----
 SPIN's advantage over SFT likely comes from its partial mitigation of exposure bias.
 SPIN doesn't only train the model to predict the next token accurately, it repeatedly trains the model to identify and fix discrepancies between its generations and the ground-truth.
 In order to do this, the model must implicitly learn to think ahead, as exposure bias is likely what causes many of the discrepancies.

 The correct prediction here might be "signs of life.". However, the model might predict "and" rather than "signs", since "and" is *reasonable* in the immediate context - it's gramatically correct, but implies a strange ending to the sentence.
 As a result, the model might end up with something like "The astronomer pointed his telescope at the distant star, hoping to see and hear." - which makes little sense.
 SPIN's advantage over SFT likely comes from its partial mitigation of exposure bias.
 SPIN doesn't only train the model to predict the next token accurately, it repeatedly trains the model to identify and fix discrepancies between its generations and the ground-truth.
 In order to do this, the model must implicitly learn to think ahead, as exposure bias is likely what causes many of the discrepancies.