Instructions to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse 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_70-quantized-deepsparse")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse") model = AutoModelForCausalLM.from_pretrained("RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse 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_70-quantized-deepsparse" # 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_70-quantized-deepsparse", "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_70-quantized-deepsparse
- SGLang
How to use RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse 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_70-quantized-deepsparse" \ --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_70-quantized-deepsparse", "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_70-quantized-deepsparse" \ --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_70-quantized-deepsparse", "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_70-quantized-deepsparse with Docker Model Runner:
docker model run hf.co/RedHatAI/Llama-2-7b-evol-code-alpaca-pruned_70-quantized-deepsparse
Create README.md
Browse files
README.md
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---
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base_model: neuralmagic/Llama-2-7b-pruned70-retrained-evolcodealpaca
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inference: false
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model_type: llama
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pipeline_tag: text-generation
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datasets:
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- cerebras/SlimPajama-627B
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- theblackcat102/evol-codealpaca-v1
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tags:
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- sparse
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- code
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- deepsparse
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---
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# Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds
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This repo contains a [70% sparse Llama 2 7B](https://huggingface.co/neuralmagic/Llama-2-7b-pruned70-retrained) finetuned for code generation tasks using the [Evolved CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset.
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It was then quantized to 8-bit weights + activations and exported to deploy with [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
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**Authors**: Neural Magic, Cerebras
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## Usage
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Below we share some code snippets on how to get quickly started with running the model.
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### Sparse Transfer
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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).
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### Running the model
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For accelerated inference with sparsity on CPUs, deploy with [deepsparse](https://github.com/neuralmagic/deepsparse).
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```python
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# pip install deepsparse[llm]
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from deepsparse import TextGeneration
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model = TextGeneration(model_path="hf:neuralmagic/Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds")
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input_text = "def fibonacci(n):\n"
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outputs = model(input_text, max_new_tokens=100)
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print(outputs.generations[0].text)
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```
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## Evaluation Benchmark Results
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Model evaluation metrics and results.
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| Benchmark | Metric | Llama-2-7b-instruct | Llama-2-7b-pruned70-retrained-evolcodealpaca-quant-ds |
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|------------------------------------------------|---------------|-------------|-------------------------------|
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| [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | xxxx | xxxx |
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## Help
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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)
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