Instructions to use MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python with Transformers:

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

pipe = pipeline("text-generation", model="MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python")
model = AutoModelForCausalLM.from_pretrained("MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python

SGLang

How to use MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python 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 "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python with Docker Model Runner:
```
docker model run hf.co/MDDDDR/Llama-3.2-1B-Instruct-FFT-coder-python
```

MDDDDR commited on Jan 14, 2025

Commit

3651c9b

verified ·

1 Parent(s): 3dff85a

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -3

README.md CHANGED Viewed

@@ -9,15 +9,19 @@ library_name: transformers
 datasets:
 - nayohan/CodeFeedback-Filtered-Instruction-ko
 ---
 - base_model : [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
 - data_set : [nayohan/CodeFeedback-Filtered-Instruction-ko](https://huggingface.co/datasets/nayohan/CodeFeedback-Filtered-Instruction-ko)
   - 해당 데이터셋을 전부 사용한건 아니며 Python언어를 우선 추출한다음 데이터셋들의 생김새를 파악, 그 다음 전처리가 공통적으로 들어갈만한 데이터를 다시 추출하여 학습에 사용했습니다.
     - 총 학습 데이터 건 : 49,859 건
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-model_id = 'MDDDDR/llama3.2-1B-Instruct-FFT-code-python'
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id,
                                              device_map="cuda:0",
@@ -79,6 +83,6 @@ print(final_output)
 # ```
 ```
-Hardware
 - A100 40GB x 1
 - Training Time : 1 hour 45 minutes

 datasets:
 - nayohan/CodeFeedback-Filtered-Instruction-ko
 ---
+### Model Card for Model ID
 - base_model : [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+### Training dataset
 - data_set : [nayohan/CodeFeedback-Filtered-Instruction-ko](https://huggingface.co/datasets/nayohan/CodeFeedback-Filtered-Instruction-ko)
   - 해당 데이터셋을 전부 사용한건 아니며 Python언어를 우선 추출한다음 데이터셋들의 생김새를 파악, 그 다음 전처리가 공통적으로 들어갈만한 데이터를 다시 추출하여 학습에 사용했습니다.
     - 총 학습 데이터 건 : 49,859 건
+### Basic usage
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = 'Llama-3.2-1B-Instruct-FFT-coder-python'
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id,
                                              device_map="cuda:0",
 # ```
 ```
+### Hardware
 - A100 40GB x 1
 - Training Time : 1 hour 45 minutes