Instructions to use ajibawa-2023/Code-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/Code-33B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/Code-33B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Code-33B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Code-33B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ajibawa-2023/Code-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/Code-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ajibawa-2023/Code-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ajibawa-2023/Code-33B

SGLang

How to use ajibawa-2023/Code-33B 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 "ajibawa-2023/Code-33B" \
    --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": "ajibawa-2023/Code-33B",
		"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 "ajibawa-2023/Code-33B" \
        --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": "ajibawa-2023/Code-33B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ajibawa-2023/Code-33B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/Code-33B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Code-33B

Large Language Models (LLMs) are good with code generations. Sometimes they do make mistakes in code generation. How about if they can give detailed explanation along with the code. This is what I have tried over here. The base Llama-1 model was used for training purpose. It is trained on around 74000 set of codes. Each set having 2 conversations. Along with Python, Java, JavaScript, GO, C++, Rust etc. code with detailed explanation is used for training purpose. It is built upon using my existing Dataset Python-Code-23k-ShareGPT. This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation.

I have released the new data Code-74k-ShareGPT on which this Model is trained.

Training:

Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took 6 days & 5 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.

This is a full fine tuned model. Links for quantized models are given below.

GPTQ GGUF & AWQ

GPTQ: Link

GGUF: Link

AWQ: Link

Extremely thankful to TheBloke for making Quantized versions of model.

Example Prompt:

This is a conversation with your helpful AI assistant. AI assistant can generate Code in various Programming Languages along with necessary explanation.

Context
You are a helpful AI assistant.

USER: <prompt>
ASSISTANT:

You can modify above Prompt as per your requirement. I have used ShareGPT/Vicuna format v1.1 .

I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.

Thank you for your love & support.

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Model tree for ajibawa-2023/Code-33B

Quantizations

4 models

ajibawa-2023
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Code-33B

Model tree for ajibawa-2023/Code-33B

Dataset used to train ajibawa-2023/Code-33B