Instructions to use braindao/iq-code-evmind-v1-granite-8b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use braindao/iq-code-evmind-v1-granite-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-v1-granite-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-v1-granite-8b-instruct")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v1-granite-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use braindao/iq-code-evmind-v1-granite-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-v1-granite-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-v1-granite-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/braindao/iq-code-evmind-v1-granite-8b-instruct

SGLang

How to use braindao/iq-code-evmind-v1-granite-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-v1-granite-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-v1-granite-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-v1-granite-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-v1-granite-8b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use braindao/iq-code-evmind-v1-granite-8b-instruct with Docker Model Runner:
```
docker model run hf.co/braindao/iq-code-evmind-v1-granite-8b-instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model_path = "braindao/iq-code-evmind-v1-granite-8b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()

chat = [
    {
        "role": "user",
        "content": "Create a smart contract to serve as a centralized review system called ReviewHub. This contract should allow users to submit and manage reviews for various products or services, rate them on a scale of 1 to 5, and provide detailed comments. It should include functionalities for assigning unique identifiers to products or services, storing and retrieving reviews, allowing users to edit or delete their reviews, calculating average ratings, and enabling an administrator to moderate content. The contract must incorporate robust security measures to ensure review integrity and prevent spam or malicious activity."
    },
]

chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
input_tokens = tokenizer(chat, return_tensors="pt")
for i in input_tokens:
    input_tokens[i] = input_tokens[i].to(device)
output = model.generate(**input_tokens, max_new_tokens=4096)
output = tokenizer.batch_decode(output)
for i in output:
    print(i)

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Safetensors

Model size

8B params

Tensor type

F16

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Quantizations

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Dataset used to train braindao/iq-code-evmind-v1-granite-8b-instruct