Instructions to use arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer

SGLang

How to use arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer 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 "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer" \
    --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": "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer",
		"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 "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer" \
        --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": "arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Mistral-7B-Instruct-v0.2-sliced-24-layer
```

Shamane commited on Apr 11, 2024

Commit

3367552

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@@ -6,35 +6,24 @@ tags:
 datasets:
 - arcee-ai/sec-data-mini
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** Arcee-ai
-- **Create from model :** mistralai/Mistral-7B-Instruct-v0.2
 ### Model Sources
-<!-- Provide the basic links for the model. -->
-- **Repository:** [https://github.com/arcee-ai/PruneMe]
-- **Paper :** [https://arxiv.org/pdf/2403.17887.pdf]
 ## Uses
-Some of the use cases - https://github.com/arcee-ai/PruneMe/tree/main?tab=readme-ov-file#use-cases
 ### Downstream Use
-Can be use to finetune. It would be nice to explore the continual pre-training as well.

 datasets:
 - arcee-ai/sec-data-mini
 ---
+## Quick Summary
+This model is an adaptation of the `mistralai/Mistral-7B-Instruct-v0.2`, refined through the application of layer pruning techniques as detailed in the paper "The Unreasonable Ineffectiveness of the Deeper Layers." It incorporates methodologies from the `MergeKit` and `PruneMe` repositories to optimize its structure, focusing on reducing redundancy within the model's deeper layers without compromising its ability to generate coherent text. The model is maintained by Arcee-ai and represents a practical implementation of computational efficiency improvements in Large Language Models (LLMs), aiming to balance performance with resource usage effectively.
 ### Model Description
+This model represents a specialized iteration of the `mistralai/Mistral-7B-Instruct-v0.2`, optimized for efficiency and performance through selective layer pruning. Developed by Arcee-ai, it leverages insights from the "The Unreasonable Ineffectiveness of the Deeper Layers" research. The pruning process was informed by the `MergeKit` and `PruneMe` tools, focusing on eliminating redundant layers to ensure a leaner, more efficient model capable of generating high-quality text outputs.
 ### Model Sources
+- **Pruning:** [PruneMe GitHub (unofficial)](https://github.com/arcee-ai/PruneMe)
+- **Paper:** ["The Unreasonable Ineffectiveness of the Deeper Layers"](https://arxiv.org/pdf/2403.17887.pdf)
+- **Merging Repository:** [MergeKit GitHub](https://github.com/arcee-ai/mergekit)
 ## Uses
+This pruned model is designed for a range of NLP tasks, with a focus on maintaining or even enhancing the model's original capabilities in generating coherent text, despite the reduction in its size. It stands as a testament to the feasibility of layer pruning in preserving the essential functional attributes of a model while offering a template for computational resource optimization.
 ### Downstream Use
+The pruned model serves as a robust foundation for fine-tuning on specific tasks and is an ideal candidate for exploring continuous pre-training opportunities. Its development is a direct application of principles outlined in "The Unreasonable Ineffectiveness of the Deeper Layers," utilizing the `MergeKit` and `PruneMe` repositories for practical pruning implementation. This model is a step forward in efficient model design, demonstrating the potential for significant reductions in computational resource requirements without detrimental effects on performance.