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 Apr 2, 2024

Commit

00f4710

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1 Parent(s): 01e78cf

Update README.md

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@@ -116,4 +116,27 @@ As I expected, it improves GSM8K, but doesn't do much to ARC.
 - Epochs: 6
 - Learning rate: 1e-5
 - Learning rate schedule: One Cycle, cosine, no cycle_momentum
-- Regularization weight: 0.1

 - Epochs: 6
 - Learning rate: 1e-5
 - Learning rate schedule: One Cycle, cosine, no cycle_momentum
+- Regularization weight: 0.1
+## Prompt format
+The format for reddit-instruct and oasst2 was:
+```
+<|user|>
+[insert instruction here]
+<|assistant|>
+[insert response here]
+<|user|>
+...
+```
+The format for TinyCoT was:
+```
+<|user|>
+[insert instruction here]
+<|rationale|>
+[insert reasoning here]
+<|answer|>
+[insert direct answer here]
+```