Instructions to use braindao/iq-code-evmind-v3.1-granite-8b-instruct-average with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use braindao/iq-code-evmind-v3.1-granite-8b-instruct-average with Transformers:

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

pipe = pipeline("text-generation", model="braindao/iq-code-evmind-v3.1-granite-8b-instruct-average")
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-v3.1-granite-8b-instruct-average")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v3.1-granite-8b-instruct-average")
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

vLLM

How to use braindao/iq-code-evmind-v3.1-granite-8b-instruct-average 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-v3.1-granite-8b-instruct-average"
# 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-v3.1-granite-8b-instruct-average",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/braindao/iq-code-evmind-v3.1-granite-8b-instruct-average

SGLang

How to use braindao/iq-code-evmind-v3.1-granite-8b-instruct-average 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-v3.1-granite-8b-instruct-average" \
    --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-v3.1-granite-8b-instruct-average",
		"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-v3.1-granite-8b-instruct-average" \
        --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-v3.1-granite-8b-instruct-average",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use braindao/iq-code-evmind-v3.1-granite-8b-instruct-average with Docker Model Runner:
```
docker model run hf.co/braindao/iq-code-evmind-v3.1-granite-8b-instruct-average
```

Overview: The braindao/iq-code-evmind-v3.1-granite-8b-instruct-average is a highly specialized language model designed to generate Solidity code efficiently and accurately. This model leverages the robust capabilities of the ibm-granite/granite-8b-code-instruct architecture, fine-tuned specifically on the braindao/Solidity-Dataset.

Dataset: The model training utilized the braindao/Solidity-Dataset, particularly focusing on the "average" column, which provides a well-balanced perspective on Solidity code examples. This dataset was meticulously curated to enhance the model's understanding and generation of high-quality Solidity code.

Capabilities:

Code Generation: Expertly generates Solidity code snippets, functions, and smart contracts.
Syntax Understanding: Displays an in-depth understanding of Solidity syntax and best practices.
Adaptability: Can adapt to various coding styles and requirements based on the provided prompts.
Efficiency: Optimized to deliver accurate and contextually relevant code outputs quickly.

Applications:

Smart Contract Development: Ideal for developers looking to create, modify, or optimize smart contracts on the Ethereum blockchain.
Educational Use: A valuable tool for learners and educators in blockchain development and Solidity programming.
Automation: Can be integrated into development workflows to automate routine coding tasks, enhancing productivity.

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Safetensors

Model size

8B params

Tensor type

F16

braindao
/

iq-code-evmind-v3.1-granite-8b-instruct-average

Dataset used to train braindao/iq-code-evmind-v3.1-granite-8b-instruct-average