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This model was contributed to Hugging Face Transformers on 2026-11-12.

IQuestCoder

IQuest-Coder-V1 is a family of code language models developed by IQuest Lab, available in 7B, 14B and 40B sizes with base, instruct and thinking variants. Its architecture is the same as Llama.

The looped IQuest-Coder-V1-*-Loop-* checkpoints use a different architecture and aren’t supported by this model.

The tokenizer is built from the checkpoints’ SentencePiece model. Many of its pieces share the same score, and SentencePiece breaks those ties by position, which the tokenizers library can’t reproduce, so a small fraction of texts tokenize differently from the original remote code.

The auto classes load it with the Llama implementation (LlamaModel and LlamaForCausalLM), so only IQuestCoderConfig is specific to IQuestCoder.

The example below demonstrates how to generate text with the AutoModelForCausalLM class.

from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("IQuestLab/IQuest-Coder-V1-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("IQuestLab/IQuest-Coder-V1-7B-Instruct", device_map="auto")

messages = [{"role": "user", "content": "Write a Python function that checks whether a number is prime."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)
output = model.generate(**inputs.to(model.device), max_new_tokens=128)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1] :], skip_special_tokens=True))

IQuestCoderConfig

class transformers.IQuestCoderConfig

< source >

( transformers_version: str | None = Nonearchitectures: list[str] | None = Noneoutput_hidden_states: bool | None = Falsereturn_dict: bool | None = Truedtype: str | torch.dtype | None = Nonechunk_size_feed_forward: int = 0is_encoder_decoder: bool = Falseid2label: dict[int, str] | dict[str, str] | None = Nonelabel2id: dict[str, int] | dict[str, str] | None = Noneproblem_type: Literal['regression', 'single_label_classification', 'multi_label_classification'] | None = Nonevocab_size: int = 76800hidden_size: int = 5120intermediate_size: int = 27648num_hidden_layers: int = 80num_attention_heads: int = 40num_key_value_heads: int | None = 8hidden_act: str = 'silu'max_position_embeddings: int = 131072initializer_range: float = 0.02rms_norm_eps: float = 1e-05use_cache: bool = Truepad_token_id: int | None = Nonebos_token_id: int | None = 1eos_token_id: int | list[int] | None = 2tie_word_embeddings: bool = Falserope_parameters: transformers.modeling_rope_utils.RopeParameters | dict | None = Noneattention_bias: bool = Falseattention_dropout: float | int = 0.0mlp_bias: bool = Falsehead_dim: int | None = 128 )

Parameters

vocab_size (int, optional, defaults to 76800) — Vocabulary size of the model. Defines the number of different tokens that can be represented by the input_ids.
hidden_size (int, optional, defaults to 5120) — Dimension of the hidden representations.
intermediate_size (int, optional, defaults to 27648) — Dimension of the MLP representations.
num_hidden_layers (int, optional, defaults to 80) — Number of hidden layers in the Transformer decoder.
num_attention_heads (int, optional, defaults to 40) — Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (int, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to num_attention_heads.
hidden_act (str, optional, defaults to silu) — The non-linear activation function (function or string) in the decoder. For example, "gelu", "relu", "silu", etc.
max_position_embeddings (int, optional, defaults to 131072) — The maximum sequence length that this model might ever be used with.
initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True or when the model is a decoder-only generative model.
pad_token_id (int, optional) — Token id used for padding in the vocabulary.
bos_token_id (int, optional, defaults to 1) — Token id used for beginning-of-stream in the vocabulary.
eos_token_id (Union[int, list[int]], optional, defaults to 2) — Token id used for end-of-stream in the vocabulary.
tie_word_embeddings (bool, optional, defaults to False) — Whether to tie weight embeddings according to model’s tied_weights_keys mapping.
rope_parameters (Union[~modeling_rope_utils.RopeParameters, dict], optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
attention_bias (bool, optional, defaults to False) — Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (Union[float, int], optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
mlp_bias (bool, optional, defaults to False) — Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
head_dim (int, optional, defaults to 128) — The attention head dimension. If None, it will default to hidden_size // num_attention_heads

This is the configuration class to store the configuration of a LlamaModel. It is used to instantiate a Iquestcoder model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the IQuestLab/IQuest-Coder-V1-40B-Instruct

Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information.

>>> from transformers import AutoModelForCausalLM, IQuestCoderConfig

>>> # Initializing an IQuest-Coder-V1-40B style configuration
>>> configuration = IQuestCoderConfig()

>>> # Initializing a model (which uses the Llama implementation) from that configuration
>>> model = AutoModelForCausalLM.from_config(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

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