Instructions to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50

SGLang

How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50 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 "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50" \
    --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": "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50",
		"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 "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50" \
        --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": "RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_50
```

Update README.md

by abhinavnmagic - opened Mar 18, 2024

base: refs/heads/main

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from: refs/pr/1

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@@ -49,11 +49,12 @@ Model evaluation metrics and results.
 | Benchmark                                      | Metric        | Llama-2-7b-evolcodealpaca  | Llama-2-7b-pruned50-retrained-evolcodealpaca |
 |------------------------------------------------|---------------|-------------|-------------------------------|
-| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | xxxx        | xxxx                          |
 ## Model Training Details
-Coming soon.
 ## Help

 | Benchmark                                      | Metric        | Llama-2-7b-evolcodealpaca  | Llama-2-7b-pruned50-retrained-evolcodealpaca |
 |------------------------------------------------|---------------|-------------|-------------------------------|
+| [HumanEval](https://arxiv.org/abs/2107.03374)  | pass@1        | 32.03       | 32.7                          |
 ## Model Training Details
+This model was obtained by sparse-tranfer of the sparse foundational model [Llama-2-7b-pruned50-retrained](https://huggingface.co/neuralmagic/Llama-2-7b-pruned50-retrained) on 60% of the [evolcodealpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.
+Training was perfomerd for 2 epochs and used the [SquareHead](https://arxiv.org/abs/2310.06927) knowledge distillation with [Llama-2-7b-evolcodealpaca](https://huggingface.co/neuralmagic/Llama-2-7b-evolcodealpaca) as teacher.
 ## Help