Instructions to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged with Transformers:

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

pipe = pipeline("text-generation", model="LimYeri/CodeMind-Llama3.1-8B-unsloth-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LimYeri/CodeMind-Llama3.1-8B-unsloth-merged")
model = AutoModelForCausalLM.from_pretrained("LimYeri/CodeMind-Llama3.1-8B-unsloth-merged")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LimYeri/CodeMind-Llama3.1-8B-unsloth-merged

SGLang

How to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged 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 "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged" \
    --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": "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged",
		"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 "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged" \
        --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": "LimYeri/CodeMind-Llama3.1-8B-unsloth-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LimYeri/CodeMind-Llama3.1-8B-unsloth-merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for LimYeri/CodeMind-Llama3.1-8B-unsloth-merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for LimYeri/CodeMind-Llama3.1-8B-unsloth-merged to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="LimYeri/CodeMind-Llama3.1-8B-unsloth-merged",
    max_seq_length=2048,
)

Docker Model Runner
How to use LimYeri/CodeMind-Llama3.1-8B-unsloth-merged with Docker Model Runner:
```
docker model run hf.co/LimYeri/CodeMind-Llama3.1-8B-unsloth-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CodeMind-Llama3.1-8B-unsloth

Codemind Project is a language model developed to assist in solving and learning coding test problems. This model is fine-tuned using posts written by LeetCode users as training data, aiming to provide answers specialized for coding tests.

Model Information

Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct
Fine-Tuning: Fine-tuned using the unsloth library based on the unsloth/Meta-Llama-3.1-8B-Instruct model
Fine-Tuning Process: Conducted with reference to the Llama 3.1 Conversational_notebook

Dataset Used

LeetCode Python Solutions Dataset: LeetCode_Python_Solutions_Data

How to Use the Model

This model is accessible through HuggingFace's model hub and can be integrated into applications using the API. It is designed to generate explanations, code snippets, or guides for coding problems or programming-related questions.

# 자세한 사항은 demo-Llama3.1.ipynb 확인
from unsloth import FastLanguageModel
from unsloth.chat_templates import get_chat_template
from IPython.display import display, Markdown

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "LimYeri/CodeMind-Llama3.1-8B-unsloth", # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

tokenizer = get_chat_template(
    tokenizer,
    chat_template = "llama-3.1",
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

messages = [
    {"role": "system", "content": "You are a kind coding test teacher."},
    {"role": "user", "content": "Enter your coding problem or question here."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")

outputs = model.generate(input_ids = inputs, max_new_tokens = 3000, use_cache = True,
                         temperature = 0.5, min_p = 0.3) # Feel free to adjust the temperature and min_p
text = (tokenizer.batch_decode(outputs))[0].split('assistant<|end_header_id|>\n\n')[1].strip()
display(Markdown(text))