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IQuest-Q1

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IQuest-Q1

Developed by IQuest

GitHub: IQuest-Q1 Hugging Face: IQuest-Q1

Introduction · Performance · Quick Start · Deployment · Limitations · Contact Us

Introduction

IQuest-Q1 is a Mixture-of-Experts (MoE) model developed by IQuest for agentic coding, reasoning, and multi-step tool use. It comprises approximately 320B total parameters, with an estimated 15B parameters activated per token.

Training pipeline of IQuest-Q1.

IQuest-Q1 Post-Training Pipeline.

Model Specifications

Property IQuest-Q1
Total Parameters 320B
Activated Parameters 15B
Transformer layers 88
Hidden dimension 3,072
Attention Heads (Q/KV) 48/8
Head Dimension 128
Hybrid Attention Pattern 3 SWA + 1 FA
Sliding Window Size 4,096
Partial RoPE Dimensions 32
Experts (Total/Activated) 256/8
MTP Layers 2 Independent (Training) / 1 Recursive x8 (Inference)
MTP Sliding Window Size 512 (Inference)
Context length 524,288

Performance

IQuest-Q1 performance across benchmarks. DeepSeek-V4-Flash/Pro refer to the official 0731/0813 releases, respectively. Humanity's Last Exam is reported without tools. IQuest-CLIBench is our in-house benchmark for CLI user experience.

Benchmark Notes

For reproducibility, we recommend using a temperature of 1.0, top-p of 0.95, and top-k of 20, with Claude Code 2.1.140 or Codex 0.142 as the respective harness.

For each model, we report the publicly reported score; otherwise, we evaluate the model using the corresponding benchmark setup: (1) Harness: For agentic coding tasks, we use mini-SWE-agent for DeepSWE v1.1, and Claude Code for other tasks. For Agents' Last Exam, we evaluate our model using Claude Code 2.1.258. Because our models currently do not have multimodal capability, we replace multimodal content inputs in the agent conversation with placeholders during tokenization. (2) Runtime: We set six-hour limits for CyberGym, eight hours for Terminal-Bench 2.1.

Quick Start

Start a server as described in Deployment, install the client with pip install openai, and then call the OpenAI-compatible API:

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="sk-iquest")
response = client.chat.completions.create(
    model="IQuest-Q1",
    messages=[
        {"role": "user", "content": "Hello! Can you briefly introduce yourself?"},
    ],
    temperature=1.0,
    top_p=0.95,
)
print(response.choices[0].message.content)

Deployment

For production deployment, we recommend using SGLang or vLLM.

SGLang

Use our prebuilt image iquestlabworkspace/sglang-iquest-q1:cu130:

docker pull iquestlabworkspace/sglang-iquest-q1:cu130

# without MTP
MODEL_ROOT="$(hf download IQuestLab/IQuest-Q1 --quiet)" && \
python -u -m sglang.launch_server \
  --model-path "$MODEL_ROOT" \
  --served-model-name IQuest-Q1 \
  --tp-size 8 \
  --dtype bfloat16 \
  --attention-backend fa3 \
  --mem-fraction-static 0.85 \
  --disable-prefill-cuda-graph \
  --enable-metrics \
  --tool-call-parser iquest_q1 \
  --reasoning-parser iquest_q1 \
  --enable-torch-compile \
  --load-format fastsafetensors \
  --speculative-use-rejection-sampling

# with recursive MTP
MODEL_ROOT="$(hf download IQuestLab/IQuest-Q1 --quiet)" && \
python -u -m sglang.launch_server \
    --model-path "$MODEL_ROOT" \
    --served-model-name IQuest-Q1 \
    --tp-size 8 \
    --dtype bfloat16 \
    --attention-backend fa3 \
    --mem-fraction-static 0.85 \
    --disable-prefill-cuda-graph \
    --enable-metrics \
    --tool-call-parser iquest_q1 \
    --reasoning-parser iquest_q1 \
    --speculative-algorithm EAGLE \
    --speculative-num-steps 5 \
    --speculative-eagle-topk 1 \
    --speculative-num-draft-tokens 6 \
    --speculative-draft-model-path "$MODEL_ROOT/mtp" \
    --enable-torch-compile \
    --load-format fastsafetensors \
    --speculative-use-rejection-sampling \
    --speculative-draft-attention-backend fa3

vLLM

Use our image iquestlabworkspace/vllm-iquest-q1:cu130:

docker pull iquestlabworkspace/vllm-iquest-q1:cu130

# without MTP
MODEL_ROOT="$(hf download IQuestLab/IQuest-Q1 --quiet)" && \
vllm serve "$MODEL_ROOT" \
  --served-model-name IQuest-Q1 \
  --tensor-parallel-size 8 \
  --reasoning-parser iquest_q1 \
  --enable-auto-tool-choice \
  --tool-call-parser iquest_q1

# with recursive MTP
MODEL_ROOT="$(hf download IQuestLab/IQuest-Q1 --quiet)" && \
vllm serve "$MODEL_ROOT" \
  --served-model-name IQuest-Q1 \
  --tensor-parallel-size 8 \
  --reasoning-parser iquest_q1 \
  --enable-auto-tool-choice \
  --tool-call-parser iquest_q1 \
  --enable-prefix-caching \
  --speculative-config '{
    "method": "eagle",
    "model": "'"$MODEL_ROOT"'/mtp",
    "num_speculative_tokens": 5,
    "draft_sample_method": "probabilistic",
    "rejection_sample_method": "standard",
    "enforce_eager": false
  }'

Tool Use and Agent Integration

Use a gateway that supports tool calling via Anthropic Messages for Claude Code and OpenAI Responses for Codex.

Claude Code

We recommend version 2.1.140 and the IQuest-Q1[1m] client-side setting does not change 512K context limit.

export ANTHROPIC_MODEL="IQuest-Q1[1m]"
export ANTHROPIC_DEFAULT_SONNET_MODEL="IQuest-Q1[1m]"
export ANTHROPIC_DEFAULT_OPUS_MODEL="IQuest-Q1[1m]"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="IQuest-Q1[1m]"
export CLAUDE_CODE_SUBAGENT_MODEL="IQuest-Q1[1m]"
export CLAUDE_CODE_MAX_OUTPUT_TOKENS="131072"
export CLAUDE_AUTOCOMPACT_PCT_OVERRIDE="80"
export CLAUDE_CODE_AUTO_COMPACT_WINDOW="524288"
export API_TIMEOUT_MS="3000000"
export CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC="1"
export CLAUDE_CODE_DISABLE_EXPERIMENTAL_BETAS="1"
export ANTHROPIC_BASE_URL="http://example-iquest-q1-link"
export ANTHROPIC_AUTH_TOKEN="sk-iquest-q1"
claude --model IQuest-Q1

Codex CLI

We recommend version 0.142.0.

export OPENAI_API_KEY="sk-iquest-q1"
export MODEL_ID="IQuest-Q1"
# Add /v1 if required by your gateway's Responses endpoint.
export BASE_URL="http://example-iquest-q1-link"

(
  set -eu
  CONFIG_DIR="${HOME}/.codex"
  mkdir -p -- "$CONFIG_DIR"
  CONFIG_DIR="$(cd -- "$CONFIG_DIR" && pwd -P)"
  # Back up existing files before replacing them.
  BACKUP_SUFFIX="$(date +%Y%m%d-%H%M%S)-$$"
  for FILE in config.toml model_catalog.json; do
    if [ -f "$CONFIG_DIR/$FILE" ]; then
      cp -p -- "$CONFIG_DIR/$FILE" "$CONFIG_DIR/$FILE.bak.$BACKUP_SUFFIX"
    fi
  done
  # This configuration disables approval prompts and sandboxing.
  cat > "$CONFIG_DIR/config.toml" <<EOF
model = "$MODEL_ID"
model_provider = "iquest"
approval_policy = "never"
sandbox_mode = "danger-full-access"
web_search = "disabled"
model_reasoning_summary = "detailed"
model_supports_reasoning_summaries = true
model_context_window = 524288
model_auto_compact_token_limit = 419430
model_auto_compact_token_limit_scope = "total"
tool_output_token_limit = 32768
model_catalog_json = "$CONFIG_DIR/model_catalog.json"
[model_providers.iquest]
name = "iquest"
base_url = "${BASE_URL%/}"
wire_api = "responses"
env_key = "OPENAI_API_KEY"
supports_websockets = false
request_max_retries = 20
stream_max_retries = 20
stream_idle_timeout_ms = 600000
[model_providers.iquest.http_headers]
max-output-tokens = "131072"
EOF
  # The gateway must support the reasoning options and max-output-tokens header.
  cat > "$CONFIG_DIR/model_catalog.json" <<EOF
{
  "models": [
    {
      "slug": "$MODEL_ID",
      "display_name": "$MODEL_ID",
      "description": "IQuest-Q1 served through an API gateway.",
      "context_window": 524288,
      "input_modalities": ["text"],
      "base_instructions": "",
      "supported_reasoning_levels": [],
      "supports_reasoning_summaries": true,
      "supports_parallel_tool_calls": false,
      "shell_type": "default",
      "tool_mode": "default",
      "truncation_policy": {
        "mode": "tokens",
        "limit": 10000
      },
      "visibility": "list",
      "supported_in_api": true,
      "priority": 1,
      "support_verbosity": false,
      "experimental_supported_tools": []
    }
  ]
}
EOF
  codex --model "$MODEL_ID"
)

Limitations

  • Text-only input. This checkpoint has no native image, audio, or video input capability.
  • Output reliability. Generated explanations and code can be incorrect. Review code changes and verify them with task-appropriate tests.
  • Tool integration. Tool calls use the IQuest-specific format in the chat template. Structured tool execution and reasoning extraction require compatible serving parsers and an agent harness.
  • Challenges in Real-World CLI Tasks. Real-world CLI tasks often require iterative debugging and verification. IQuest-Q1 may overlook constraints, repeat failed attempts, or leave issues unresolved, necessitating human oversight.
  • Ongoing development. IQuest-Q1 remains at an early stage, with substantial limitations in its capabilities and reliability. Much work remains, and we still have a long way to go.

Contact Us

For technical questions, feedback, or collaboration inquiries, please email us at research@iquestlab.com.

IQuest

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