Instructions to use Marker689/IQuest-Coder-V1-40B-Loop-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marker689/IQuest-Coder-V1-40B-Loop-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Marker689/IQuest-Coder-V1-40B-Loop-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Marker689/IQuest-Coder-V1-40B-Loop-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Marker689/IQuest-Coder-V1-40B-Loop-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Marker689/IQuest-Coder-V1-40B-Loop-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": "Marker689/IQuest-Coder-V1-40B-Loop-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Marker689/IQuest-Coder-V1-40B-Loop-Instruct
- SGLang
How to use Marker689/IQuest-Coder-V1-40B-Loop-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 "Marker689/IQuest-Coder-V1-40B-Loop-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": "Marker689/IQuest-Coder-V1-40B-Loop-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 "Marker689/IQuest-Coder-V1-40B-Loop-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": "Marker689/IQuest-Coder-V1-40B-Loop-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Marker689/IQuest-Coder-V1-40B-Loop-Instruct with Docker Model Runner:
docker model run hf.co/Marker689/IQuest-Coder-V1-40B-Loop-Instruct
File size: 969 Bytes
8f28c3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | {
"_name_or_path": "iquestloopcoder",
"architectures": [
"IQuestLoopCoderForCausalLM"
],
"model_type": "iquestloopcoder",
"vocab_size": 76800,
"hidden_size": 5120,
"intermediate_size": 27648,
"num_hidden_layers": 80,
"eos_token_id": [2, 75864, 75869],
"num_attention_heads": 40,
"num_key_value_heads": 8,
"head_dim": 128,
"hidden_act": "silu",
"max_position_embeddings": 131072,
"initializer_range": 0.02,
"rms_norm_eps": 1e-05,
"use_cache": true,
"tie_word_embeddings": false,
"rope_theta": 500000,
"attention_bias": false,
"attention_dropout": 0.0,
"mlp_bias": false,
"loop_num": 2,
"loop_window_size": 64,
"torch_dtype": "bfloat16",
"transformers_version": "4.55.4",
"auto_map": {
"AutoConfig": "configuration_iquestloopcoder.IQuestLoopCoderConfig",
"AutoModel": "modeling_iquestloopcoder.IQuestLoopCoderModel",
"AutoModelForCausalLM": "modeling_iquestloopcoder.IQuestLoopCoderForCausalLM"
}
}
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