Instructions to use braindao/iq-code-evmind-v2-llama3-code-8b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use braindao/iq-code-evmind-v2-llama3-code-8b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="braindao/iq-code-evmind-v2-llama3-code-8b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("braindao/iq-code-evmind-v2-llama3-code-8b-instruct")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v2-llama3-code-8b-instruct")
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 braindao/iq-code-evmind-v2-llama3-code-8b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "braindao/iq-code-evmind-v2-llama3-code-8b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "braindao/iq-code-evmind-v2-llama3-code-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/braindao/iq-code-evmind-v2-llama3-code-8b-instruct

SGLang

How to use braindao/iq-code-evmind-v2-llama3-code-8b-instruct 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 "braindao/iq-code-evmind-v2-llama3-code-8b-instruct" \
    --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": "braindao/iq-code-evmind-v2-llama3-code-8b-instruct",
		"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 "braindao/iq-code-evmind-v2-llama3-code-8b-instruct" \
        --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": "braindao/iq-code-evmind-v2-llama3-code-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use braindao/iq-code-evmind-v2-llama3-code-8b-instruct with Docker Model Runner:
```
docker model run hf.co/braindao/iq-code-evmind-v2-llama3-code-8b-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is a finely-tuned version specifically designed to generate and resolve queries related to the Solidity programming language. This model has been developed from the robust foundation provided by ajibawa-2023/Code-Llama-3-8B and has undergone specialized fine-tuning to optimize its performance in tasks associated with Solidity, the primary language used for developing smart contracts on the Ethereum blockchain.

Key Features:

Solidity Code Generation: The model can generate Solidity code snippets, offering quick and accurate solutions for various development needs. Query Resolution: It answers technical and conceptual questions about Solidity, covering basic concepts to advanced topics, facilitating learning and problem-solving. Customized Optimization: The fine-tuning ensures the model is optimized to handle specific contexts and nuances of Solidity, providing more relevant and detailed responses. Applications:

Smart Contract Development: Assists developers in creating, optimizing, and debugging smart contracts in Solidity. Education and Training: Serves as an educational tool for those looking to learn Solidity, providing clear explanations and practical examples. Technical Assistance: Acts as a virtual technical assistant, answering queries and providing solutions to complex issues in smart contract development. Base Model:

This model is based on ajibawa-2023/Code-Llama-3-8B, known for its advanced code generation capabilities and deep understanding of programming languages.

How to Use:

You can integrate this model into your projects via the Hugging Face platform, utilizing the provided APIs and tools to facilitate its implementation and use in various applications.

Example Usage:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-username/iq-code-evmind-v1-code-llama3-8b-instruct-pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "How can I define a basic contract structure in Solidity?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With iq-code-evmind-v1-code-llama3-8b-instruct-pro, you will have a powerful and specialized tool to handle everything related to Solidity development, from code generation to technical query resolution.

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Safetensors

Model size

8B params

Tensor type

F16

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