Instructions to use TheBloke/CodeLlama-13B-Python-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/CodeLlama-13B-Python-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/CodeLlama-13B-Python-GPTQ", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/CodeLlama-13B-Python-GPTQ", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-13B-Python-GPTQ", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TheBloke/CodeLlama-13B-Python-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/CodeLlama-13B-Python-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/CodeLlama-13B-Python-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/CodeLlama-13B-Python-GPTQ

SGLang

How to use TheBloke/CodeLlama-13B-Python-GPTQ 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 "TheBloke/CodeLlama-13B-Python-GPTQ" \
    --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": "TheBloke/CodeLlama-13B-Python-GPTQ",
		"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 "TheBloke/CodeLlama-13B-Python-GPTQ" \
        --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": "TheBloke/CodeLlama-13B-Python-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/CodeLlama-13B-Python-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/CodeLlama-13B-Python-GPTQ
```

TheBloke commited on Aug 26, 2023

Commit

3f4944a

1 Parent(s): 47235a9

Initial GPTQ model commit

Browse files

Files changed (1) hide show

README.md +7 -3

README.md CHANGED Viewed

@@ -47,10 +47,12 @@ Multiple GPTQ parameter permutations are provided; see Provided Files below for
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGML)
 * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
-## Prompt template: TBC
 ```
-Info on prompt template will be added shortly.
 ```
 ## Provided files and GPTQ parameters
@@ -159,7 +161,9 @@ model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
 """
 prompt = "Tell me about AI"
-prompt_template=f'''Info on prompt template will be added shortly.
 '''
 print("\n\n*** Generate:")

 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/CodeLlama-13B-Python-GGML)
 * [Meta's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/codellama/CodeLlama-13b-python-hf)
+## Prompt template: CodeLlama
 ```
+[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
+{prompt}
+[/INST]
 ```
 ## Provided files and GPTQ parameters
 """
 prompt = "Tell me about AI"
+prompt_template=f'''[INST] Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```:
+{prompt}
+[/INST]
 '''
 print("\n\n*** Generate:")