Instructions to use RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits

SGLang

How to use RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits 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 "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits" \
    --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": "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits",
		"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 "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits" \
        --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": "RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

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Llama-2-7b-evolcodealpaca - bnb 8bits

Model creator: https://huggingface.co/neuralmagic/
Original model: https://huggingface.co/neuralmagic/Llama-2-7b-evolcodealpaca/

Original model description:

base_model: meta-llama/Llama-2-7b-hf inference: true model_type: llama pipeline_tag: text-generation datasets: - theblackcat102/evol-codealpaca-v1 tags: - code

Llama-2-7b-evolcodealpaca

This repo contains a Llama 2 7B finetuned for code generation tasks using the Evolved CodeAlpaca dataset.

Official model weights from Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment.

Authors: Neural Magic, Cerebras

Usage

Below we share some code snippets on how to get quickly started with running the model.

Sparse Transfer

By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process here.

Running the model

This model may be run with the transformers library. For accelerated inference with sparsity, deploy with nm-vllm or deepsparse.

# pip install transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-evolcodealpaca")
model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-evolcodealpaca", device_map="auto")

input_text = "def fibonacci(n):\n"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Evaluation Benchmark Results

Model evaluation metrics and results.

Benchmark	Metric	Llama-2-7b-evolcodealpaca
HumanEval	pass@1	32.03

Model Training Details

Coming soon.

Help

For further support, and discussions on these models and AI in general, join Neural Magic's Slack Community

Downloads last month: 5

Safetensors

Model size

7B params

Tensor type

F32

F16

Papers for RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits

Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment

Paper • 2405.03594 • Published May 6, 2024 • 7

Evaluating Large Language Models Trained on Code

Paper • 2107.03374 • Published Jul 7, 2021 • 11