- Libraries
- Transformers
How to use braindao/iq-code-evmind-v3.2-gemma2-9b-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-v3.2-gemma2-9b-instruct-average")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("braindao/iq-code-evmind-v3.2-gemma2-9b-instruct-average")
model = AutoModelForCausalLM.from_pretrained("braindao/iq-code-evmind-v3.2-gemma2-9b-instruct-average", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use braindao/iq-code-evmind-v3.2-gemma2-9b-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-v3.2-gemma2-9b-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-v3.2-gemma2-9b-instruct-average",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/braindao/iq-code-evmind-v3.2-gemma2-9b-instruct-average
- SGLang
How to use braindao/iq-code-evmind-v3.2-gemma2-9b-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-v3.2-gemma2-9b-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-v3.2-gemma2-9b-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-v3.2-gemma2-9b-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-v3.2-gemma2-9b-instruct-average",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Docker Model Runner
How to use braindao/iq-code-evmind-v3.2-gemma2-9b-instruct-average with Docker Model Runner:
docker model run hf.co/braindao/iq-code-evmind-v3.2-gemma2-9b-instruct-average
