Instructions to use ajibawa-2023/Python-Code-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ajibawa-2023/Python-Code-13B with Transformers:

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

pipe = pipeline("text-generation", model="ajibawa-2023/Python-Code-13B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/Python-Code-13B")
model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/Python-Code-13B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ajibawa-2023/Python-Code-13B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ajibawa-2023/Python-Code-13B"
# 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/Python-Code-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ajibawa-2023/Python-Code-13B

SGLang

How to use ajibawa-2023/Python-Code-13B 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/Python-Code-13B" \
    --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/Python-Code-13B",
		"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/Python-Code-13B" \
        --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/Python-Code-13B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ajibawa-2023/Python-Code-13B with Docker Model Runner:
```
docker model run hf.co/ajibawa-2023/Python-Code-13B
```

ajibawa-2023 commited on Nov 11, 2023

Commit

2330aac

1 Parent(s): 6ad1c63

Update README.md

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@@ -4,4 +4,35 @@ datasets:
 - ajibawa-2023/Python-Code-23k-ShareGPT
 language:
 - en
----

 - ajibawa-2023/Python-Code-23k-ShareGPT
 language:
 - en
+---
+**Python-Code-13B**
+Large Language Models (LLMs) are good with code generations. Sometimes LLMs 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-2 model was used for training purpose. It is trained on around 23000+ set of codes. Each set having 2 conversations.
+This conversation is in Vicuna/ShareGPT format. Each set, along with code, has detailed explanation.
+We have released the [data](https://huggingface.co/datasets/ajibawa-2023/Python-Code-23k-ShareGPT).
+**Training:**
+Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 13 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-2 by Meta.
+**GPTQ GGML & AWQ**
+GPTQ: TBA
+GGUF: TBA
+AWQ: TBA
+**Example Prompt:**
+```
+This is a conversation with your helpful AI assistant. AI assistant can generate Python Code along with necessary explanation.
+Context
+You are a helpful AI assistant.
+USER: <prompt>
+ASSISTANT:
+```