Instructions to use codellama/CodeLlama-70b-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codellama/CodeLlama-70b-hf with Transformers:

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

pipe = pipeline("text-generation", model="codellama/CodeLlama-70b-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-70b-hf")
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-70b-hf")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codellama/CodeLlama-70b-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codellama/CodeLlama-70b-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codellama/CodeLlama-70b-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codellama/CodeLlama-70b-hf

SGLang

How to use codellama/CodeLlama-70b-hf 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 "codellama/CodeLlama-70b-hf" \
    --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": "codellama/CodeLlama-70b-hf",
		"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 "codellama/CodeLlama-70b-hf" \
        --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": "codellama/CodeLlama-70b-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codellama/CodeLlama-70b-hf with Docker Model Runner:
```
docker model run hf.co/codellama/CodeLlama-70b-hf
```

cuda out of memory exceptions

by sanipanwala - opened Feb 27, 2024

Discussion

sanipanwala

Feb 27, 2024

Hello,

I have 2 GPU of 24 GB RTX 4090 GPU.

I want to fine-tune the 70b model but it throws a cuda out of memory exceptions even though I have used Lora and BitsAndBytesConfig.

Let me know if I'm overlooking this or please give me suggestions.

Thanks.

faroncoder

Feb 29, 2024

I wondered whether you have tried 13b or 40b work before you moved up to 70b?

Possibility: Are you using a 32-bit floating point for training or inference? If so, consider switching to FP16, a 16-bit floating point.

Maybe emptying the memory cache would help by using torch.cuda.empty_cache() ?

sanipanwala

Mar 1, 2024

Hi @faroncoder ,

Thanks for your reply.

Yes I tried with 13b and it is working fine.
Yes, I'm using FP16 and torch.cuda.empty_cache().

Please check my Lora and BitsAndBytesConfig configuration.

bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
)
config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=modules,
lora_dropout=0.1,
bias="none",
task_type="CAUSAL_LM",
)

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