Instructions to use IQuestLab/IQuest-Coder-V1-14B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IQuestLab/IQuest-Coder-V1-14B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IQuestLab/IQuest-Coder-V1-14B-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("IQuestLab/IQuest-Coder-V1-14B-Instruct", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IQuestLab/IQuest-Coder-V1-14B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IQuestLab/IQuest-Coder-V1-14B-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": "IQuestLab/IQuest-Coder-V1-14B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/IQuestLab/IQuest-Coder-V1-14B-Instruct
- SGLang
How to use IQuestLab/IQuest-Coder-V1-14B-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 "IQuestLab/IQuest-Coder-V1-14B-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": "IQuestLab/IQuest-Coder-V1-14B-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 "IQuestLab/IQuest-Coder-V1-14B-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": "IQuestLab/IQuest-Coder-V1-14B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use IQuestLab/IQuest-Coder-V1-14B-Instruct with Docker Model Runner:
docker model run hf.co/IQuestLab/IQuest-Coder-V1-14B-Instruct
| Loaded loader_megatron_core as the loader. | |
| Loaded saver_llama2_hf_bf as the saver. | |
| Starting saver... | |
| Starting loader... | |
| fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function. | |
| /usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import apex plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin. | |
| warnings.warn( | |
| /usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import huggingface plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin. | |
| warnings.warn( | |
| /usr/local/lib/python3.12/dist-packages/modelopt/torch/utils/import_utils.py:31: UserWarning: Failed to import megatron plugin due to: AttributeError("module 'transformers.modeling_utils' has no attribute 'Conv1D'"). You may ignore this warning if you do not need this plugin. | |
| warnings.warn( | |
| Setting num_layers to 28 from checkpoint | |
| Setting hidden_size to 5120 from checkpoint | |
| Setting ffn_hidden_size to 27648 from checkpoint | |
| Setting seq_length to 131072 from checkpoint | |
| Setting num_attention_heads to 40 from checkpoint | |
| Setting num_query_groups to 8 from checkpoint | |
| Setting group_query_attention to True from checkpoint | |
| Setting kv_channels to 128 from checkpoint | |
| Setting max_position_embeddings to 131072 from checkpoint | |
| Setting position_embedding_type to rope from checkpoint | |
| Setting add_position_embedding to True from checkpoint | |
| Setting use_rotary_position_embeddings to True from checkpoint | |
| Setting rotary_base to 500000 from checkpoint | |
| Setting rotary_percent to 1.0 from checkpoint | |
| Setting rotary_interleaved to False from checkpoint | |
| Setting add_bias_linear to False from checkpoint | |
| Setting add_qkv_bias to False from checkpoint | |
| Setting squared_relu to False from checkpoint | |
| Setting swiglu to True from checkpoint | |
| Setting untie_embeddings_and_output_weights to True from checkpoint | |
| Setting apply_layernorm_1p to False from checkpoint | |
| Setting normalization to RMSNorm from checkpoint | |
| Setting apply_query_key_layer_scaling to False from checkpoint | |
| Setting attention_dropout to 0.0 from checkpoint | |
| Setting hidden_dropout to 0.0 from checkpoint | |
| Checkpoint did not provide arguments hybrid_override_pattern | |
| Checkpoint did not provide arguments spec | |
| Setting hybrid_attention_ratio to 0.0 from checkpoint | |
| Setting hybrid_mlp_ratio to 0.0 from checkpoint | |
| Checkpoint did not provide arguments num_experts | |
| Setting moe_layer_freq to 1 from checkpoint | |
| Setting moe_router_topk to 2 from checkpoint | |
| Setting moe_router_pre_softmax to False from checkpoint | |
| Setting moe_grouped_gemm to False from checkpoint | |
| Checkpoint did not provide arguments moe_shared_expert_intermediate_size | |
| Setting mamba_state_dim to 128 from checkpoint | |
| Setting mamba_head_dim to 64 from checkpoint | |
| Setting mamba_num_groups to 8 from checkpoint | |
| Checkpoint did not provide arguments mamba_num_heads | |
| Setting is_hybrid_model to False from checkpoint | |
| Checkpoint did not provide arguments heterogeneous_layers_config_path | |
| Checkpoint did not provide arguments heterogeneous_layers_config_encoded_json | |
| Setting tokenizer_type to SFTTokenizer from checkpoint | |
| Setting tokenizer_model to /cpfs01/users/wzhang/iquest-coder-v1.1/RepoData-Ucoder-32B-128k-from2.5.2/97.09B_instruct_iquest-coder from checkpoint | |
| Checkpoint did not provide arguments tiktoken_pattern | |
| Setting padded_vocab_size to 76800 from checkpoint | |
| INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1 | |
| WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it | |
| building GPT model ... | |
| (TP, PP) mismatch after resume ((1, 1) vs (8, 1) from checkpoint): RNG state will be ignored | |
| sharded_state_dict metadata loaded from the checkpoint: {'distrib_optim_sharding_type': 'dp_reshardable', 'singleton_local_shards': False, 'chained_optim_avoid_prefix': True} | |
| Job sharding has changed: Rerun state will be ignored | |
| loading distributed checkpoint from /tmp/megatron_convert_iter1616_node0_pid360_42a53cb4 at iteration 1616 | |
| /volume/pt-train/users/wzhang/wjj-workspace/code-sft/src/training/Megatron-LM/megatron/core/dist_checkpointing/strategies/torch.py:956: FutureWarning: `load_state_dict` is deprecated and will be removed in future versions. Please use `load` instead. | |
| checkpoint.load_state_dict( | |
| /usr/local/lib/python3.12/dist-packages/torch/distributed/checkpoint/planner_helpers.py:406: FutureWarning: Please use DTensor instead and we are deprecating ShardedTensor. | |
| device = getattr(value, "device", None) | |
| /usr/local/lib/python3.12/dist-packages/torch/distributed/checkpoint/default_planner.py:454: FutureWarning: Please use DTensor instead and we are deprecating ShardedTensor. | |
| and md.size != obj.size() | |
| checkpoint version 3.0 | |
| successfully loaded checkpoint from /tmp/megatron_convert_iter1616_node0_pid360_42a53cb4 [ t 1/1, p 1/1 ] at iteration 1616 | |
| sending embeddings | |
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| Waiting for saver to complete... | |
| fused_indices_to_multihot has reached end of life. Please migrate to a non-experimental function. | |
| received embeddings | |
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| Saving model to disk ... | |