Text Generation
Transformers
PyTorch
llama
code llama
Eval Results (legacy)
text-generation-inference
Instructions to use Phind/Phind-CodeLlama-34B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Phind/Phind-CodeLlama-34B-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Phind/Phind-CodeLlama-34B-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Phind/Phind-CodeLlama-34B-v2") model = AutoModelForCausalLM.from_pretrained("Phind/Phind-CodeLlama-34B-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Phind/Phind-CodeLlama-34B-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Phind/Phind-CodeLlama-34B-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Phind/Phind-CodeLlama-34B-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Phind/Phind-CodeLlama-34B-v2
- SGLang
How to use Phind/Phind-CodeLlama-34B-v2 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 "Phind/Phind-CodeLlama-34B-v2" \ --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": "Phind/Phind-CodeLlama-34B-v2", "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 "Phind/Phind-CodeLlama-34B-v2" \ --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": "Phind/Phind-CodeLlama-34B-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Phind/Phind-CodeLlama-34B-v2 with Docker Model Runner:
docker model run hf.co/Phind/Phind-CodeLlama-34B-v2
Download pytorch_model-00005-of-00007.bin from Phind/Phind-CodeLlama-34B-v2: direct link, hf CLI and curl.
- Browser
- Download file 9.69 GB
-
https://huggingface.co/Phind/Phind-CodeLlama-34B-v2/resolve/refs%2Fpr%2F31/pytorch_model-00005-of-00007.bin
- Command line
-
hf download hf://Phind/Phind-CodeLlama-34B-v2@refs/pr/31/pytorch_model-00005-of-00007.bin
-
curl -L -o pytorch_model-00005-of-00007.bin https://huggingface.co/Phind/Phind-CodeLlama-34B-v2/resolve/refs%2Fpr%2F31/pytorch_model-00005-of-00007.bin
9.69 GB
- Xet hash:
- 688af8b2b7aae743decac7bf67fc543f2c59fd8d50de24ab2156d098598db886
- Size of remote file:
- 9.69 GB
- SHA256:
- 69382adfde4916bc40af76d87a9fe9294b7ed2ad936fb9efed2452d798e1982b
·
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