Instructions to use Phind/Phind-CodeLlama-34B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Smaller model sizes to allow for speculative decoding

by wrtn2 - opened Sep 4, 2023

Discussion

wrtn2

Sep 4, 2023

This is excellent, really impressive! Are there any plans to release smaller model sizes? For example 3B or 7B trained against the same data set could be useful for speculative decoding optimization. TIA

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