Instructions to use emre/llama-2-13b-code-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emre/llama-2-13b-code-chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="emre/llama-2-13b-code-chat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("emre/llama-2-13b-code-chat") model = AutoModelForCausalLM.from_pretrained("emre/llama-2-13b-code-chat") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use emre/llama-2-13b-code-chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "emre/llama-2-13b-code-chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "emre/llama-2-13b-code-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/emre/llama-2-13b-code-chat
- SGLang
How to use emre/llama-2-13b-code-chat 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 "emre/llama-2-13b-code-chat" \ --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": "emre/llama-2-13b-code-chat", "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 "emre/llama-2-13b-code-chat" \ --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": "emre/llama-2-13b-code-chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use emre/llama-2-13b-code-chat with Docker Model Runner:
docker model run hf.co/emre/llama-2-13b-code-chat
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("emre/llama-2-13b-code-chat")
model = AutoModelForCausalLM.from_pretrained("emre/llama-2-13b-code-chat")Quick Links
π¦π» CodeLlama
emre/llama-2-13b-code-chat is a Llama 2 version of CodeAlpaca.
π§ Training
This model is based on the llama-2-13b-chat-hf model, fine-tuned using QLoRA on the mlabonne/CodeLlama-2-20k dataset.
It was trained on an Colab Pro+It was trained Colab Pro+.
It is mainly designed for educational purposes, not for inference but can be used exclusively with BBVA Group, GarantiBBVA and its subsidiaries.
π» Usage
# pip install transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "emre/llama-2-13b-code-chat"
prompt = "Write Python code to generate an array with all the numbers from 1 to 100"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
sequences = pipeline(
f'<s>[INST] {prompt} [/INST]',
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=200,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
Ouput:
Here is a Python code to generate an array with all the numbers from 1 to 100:
β
```
numbers = []
for i in range(1,101):
numbers.append(i)
β
```
This code generates an array with all the numbers from 1 to 100 in Python. It uses a loop that iterates over the range of numbers from 1 to 100, and for each number, it appends that number to the array 'numbers'. The variable 'numbers' is initialized to a list, and its length is set to 101 by using the range of numbers (0-99).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="emre/llama-2-13b-code-chat")