Instructions to use BallisticAI/Ballistic-CodeLlama-34B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BallisticAI/Ballistic-CodeLlama-34B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="BallisticAI/Ballistic-CodeLlama-34B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BallisticAI/Ballistic-CodeLlama-34B-v1")
model = AutoModelForCausalLM.from_pretrained("BallisticAI/Ballistic-CodeLlama-34B-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BallisticAI/Ballistic-CodeLlama-34B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BallisticAI/Ballistic-CodeLlama-34B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BallisticAI/Ballistic-CodeLlama-34B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BallisticAI/Ballistic-CodeLlama-34B-v1

SGLang

How to use BallisticAI/Ballistic-CodeLlama-34B-v1 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 "BallisticAI/Ballistic-CodeLlama-34B-v1" \
    --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": "BallisticAI/Ballistic-CodeLlama-34B-v1",
		"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 "BallisticAI/Ballistic-CodeLlama-34B-v1" \
        --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": "BallisticAI/Ballistic-CodeLlama-34B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BallisticAI/Ballistic-CodeLlama-34B-v1 with Docker Model Runner:
```
docker model run hf.co/BallisticAI/Ballistic-CodeLlama-34B-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CodeLlama 34B v1

Model creator: BallisticAI
Based on: CodeLlama 34B hf
Merged with: CodeLlama 34B v2 && speechless-codellama-34b-v2
Additional training with: jondurbin/airoboros-2.2

Description

This repo contains model for Ballistic-CodeLlama-34B-v1.

Repositories available

How to Prompt the Model

This model accepts the Alpaca/Vicuna instruction format.

For example:

### System Prompt
You are an intelligent programming assistant.

### User Message
Implement a linked list in C++

### Assistant
...

Bias, Risks, and Limitations

This model has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.

Thanks

Thanks to:

The Original Llama team
Phind
uukuguy
jondurbin
And everyone else who's involved in the Open Source AI/ML Community.

Downloads last month: 6

Safetensors

Model size

34B params

Tensor type

F16

Model tree for BallisticAI/Ballistic-CodeLlama-34B-v1

Unable to build the model tree, the base model loops to the model itself. Learn more.

Evaluation results

n/a on HumanEval
self-reported

n/a