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
```

neuralmagic_-_Llama-2-7b-evolcodealpaca-8bits / README.md

RichardErkhov

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	Quantization made by Richard Erkhov.

	[Github](https://github.com/RichardErkhov)

	[Discord](https://discord.gg/pvy7H8DZMG)

	[Request more models](https://github.com/RichardErkhov/quant_request)


	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](https://huggingface.co/meta-llama/Llama-2-7b-hf) finetuned for code generation tasks using the [Evolved CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.

	Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594).

	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](https://neuralmagic.github.io/docs-v2/get-started/transfer).

	### Running the model

	This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse).

	```python
	# 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](https://arxiv.org/abs/2107.03374) \| 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](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ)