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sriq-ai
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Model Details
Model Provider
sriq-ai
Model Tree
XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B
Input Modalities
Output Modalities
Supported Functionality
text
<think>设 s=x+y，p=xy。x²+y²=s²-2p=13。…s=2,5。验：s=5，x,y=2,3，平方和13，立方和35。答案对。</think>x + y = 2 or 5. <full answer in the question's language>v1.6 is the first dataset version where the check can catch something. 19.5% of its rows
plant a real mistake in one step and have the 验： block find it and redo it
(不对。重新检查…). The rest confirm. It also scales trace length with difficulty
(median reasoning 25 / 140 / 254 tokens for easy / medium / hard), and 5,000 of its 9,988
rows sit behind a long coding-harness system prompt, so the Chinese reasoning is trained in
the context where it otherwise drifts back to English.
No benchmarks were run for this release. Nothing here measures accuracy, the share of rows that reason in Chinese, verification rate or looping against the base model. Evaluate it on your own workload before relying on it.
The checkpoint is Qwen3_5ForConditionalGeneration (the vision tower is kept), so load it
with AutoModelForImageTextToText:
python
import torchfrom transformers import AutoModelForImageTextToText, AutoTokenizer model_id = "sriq-ai/sriq-mimo_v2.6-9b-v1.6"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto").eval() messages = [{"role": "user", "content": "What is 17 * 23? Answer briefly."}]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=1024)print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))generation_config.json ships the base's sampling defaults: temperature 0.6, top-p 0.95,
top-k 20.
enable_thinking=FalseThe chat template treats that flag as an instruction to skip reasoning and prefills an empty block:
jinja
{%- if enable_thinking is false -%} {{- '<think></think>' -}}{%- endif -%}The model then answers with no trace, silently. There is no error, and the behaviour this
model exists for is switched off. Leave the flag undefined, or set it to True.
About 19.3 GB of weights (9.41B parameters, plus a 0.24B MTP head).
Compatible with FriendliAI Dedicated Endpoints. The repository
matches the Qwen/Qwen3.5-9B layout tensor for tensor (775 tensors, same dtypes and
shapes, same config.json), so it deploys the same way Qwen3.5-9B does. The checkpoint as
first uploaded did not deploy there. FIX.md lists what changed and why.
The base model's card documents SGLang with --reasoning-parser mimo. The architecture and
chat template are unchanged by the merge, so the same command should apply, but it has not
been checked.
GGUF quantizations (Q2_K_L through Q8_0, F16, BF16, plus the mmproj file) are at
sriq-ai/sriq-mimo_v2.6-9b-v1.6-GGUF:
llama-mtmd-cli -hf sriq-ai/sriq-mimo_v2.6-9b-v1.6-GGUF --jinja.
| Base model | XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (SFT of Qwen/Qwen3.5-9B) |
| Parameters | 9,409,813,744 (+ 243,290,624 in the MTP head, taken from Qwen3.5-9B) |
| Architecture | Qwen3_5ForConditionalGeneration, 32 layers (3 linear-attention : 1 full-attention) |
| Weights | bfloat16 (linear-attention A_log and norm in float32, as in Qwen3.5), merged (not an adapter) |
| Context length | 262,144 (max_position_embeddings) |
| Training data | sriq-ai/sriq-sft-v1.6: 9,988 conversations, 30 languages, easy / medium / hard |
Supervised fine-tuning with Unsloth and TRL, LoRA merged into the bf16 base.
| rank / alpha / dropout | 16 / 16 / 0 |
| target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| LoRA variants | rsLoRA, DoRA and LoftQ all off |
| steps | 200 (max_steps; the run is step-capped, not epoch-capped) |
| effective batch size |
Hardware is not recorded on this card.
Apache-2.0. The base model, XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B, is released under MIT; its copyright notice applies to the base weights.
Developed by SRIQ. Base model by the Xiaomi MiMo team, on Qwen3.5-9B by the Qwen team.
<think>设 s=x+y，p=xy。x²+y²=s²-2p=13。…s=2,5。验：s=5，x,y=2,3，平方和13，立方和35。答案对。</think>x + y = 2 or 5. <full answer in the question's language>{%- if enable_thinking is false -%} {{- '<think></think>' -}}{%- endif -%}| 8 (per-device 2 × gradient accumulation 4) |
| epoch fraction | ~0.16 (1,600 of 9,988 rows) |
| learning rate | 2e-4, linear schedule, 3 warmup steps |
| optimizer | adamw_8bit, weight decay 0.001 |
| max sequence length | 65,536, no packing, loss on completions only |
| gradient checkpointing | Unsloth |
| seed | 3407 |