metadata

base_model:
  - Pinkstack/SuperThoughts-CoT-14B-16k-o1-QwQ
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
  - code
  - phi3
  - cot
  - o1
  - reasoning
  - cot
license: mit
license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE
language:
  - en
  - multilingual
pipeline_tag: text-generation
inference:
  parameters:
    temperature: 0.3
widget:
  - messages:
      - role: user
        content: How many R's in strawberry? Think step by step.
model-index:
  - name: SuperThoughts-CoT-14B-16k-o1-QwQ
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: wis-k/instruction-following-eval
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 5.15
            name: averaged accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: SaylorTwift/bbh
          split: test
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 52.85
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: lighteval/MATH-Hard
          split: test
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 40.79
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          split: train
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 19.02
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 21.79
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 47.43
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=Pinkstack%2FSuperThoughts-CoT-14B-16k-o1-QwQ
          name: Open LLM Leaderboard

safetensors version: Pinkstack/SuperThoughts-CoT-14B-16k-o1-QwQ Phi-4 Technical Report (SuperThoughts 14B is based on phi-4)

We are very proud to announce, SuperThoughts, but you can just call it o1 mini 😉

Please check the examples we provided: https://huggingface.co/Pinkstack/SuperThoughts-CoT-14B-16k-o1-QwQ-GGUF#%F0%9F%A7%80-examples Beats qwen/qwq at MATH & MuSR (MuSR being a reasoning benchmark) Evaluation: Please note, the low IFEVAL results is most likely due to the model always providing reasoning, prompt following is not great with this model sadly.

Unlike previous models we've uploaded, this one is the best one we've published! Answers in two steps: Reasoning -> Final answer like o1 mini and other similar reasoning ai models.

🧀 Which quant is right for you? (all tested!)

Q3: This quant should be used on most high-end devices like rtx 2080TI's, Responses are very high quality, but its slightly slower than Q4. (Runs at ~1 tokens per second or less on a Samsung z fold 5 smartphone.)
Q4: This quant should be used on high-end modern devices like rtx 3080's or any GPU,TPU,CPU that is powerful enough and has at minimum 15gb of available memory, (On servers and high-end computers we personally use it.) reccomened.
Q8: This quant should be used on very high-end modern devices which can handle it's power, it is very powerful but q4 is more well rounded, not recommended.

Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	31.17
IFEval (0-Shot)	5.15
BBH (3-Shot)	52.85
MATH Lvl 5 (4-Shot)	40.79
GPQA (0-shot)	19.02
MuSR (0-shot)	21.79
MMLU-PRO (5-shot)	47.43

the model uses this prompt format: (modified phi-4 prompt)

{{ if .System }}<|system|>
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|user|>
{{ .Prompt }}<|im_end|>
{{ end }}<|assistant|>{{ .CoT }}<|CoT|>
{{ .Response }}<|FinalAnswer|><|im_end|>

It is recommended to use a system prompt like this one:

You are a helpful ai assistant. Make sure to put your finalanswer at the end.

🧀 Examples:

(q4_k_m, 10GB rtx 3080, 64GB memory, running inside of MSTY, all use "You are a friendly ai assistant." as the System prompt.) example 1: example 2: example 3: example 4:

All generated locally and pretty quickly too! 😲 Due to our very limited resources we weren't able to evaluate this model (yet..) if you evaluate it please do let us know!

🧀 Information

⚠️ A low temperature must be used to ensure it won't fail at reasoning. we use 0.3 - 0.8!
⚠️ Due to the current prompt format, it may sometimes put <|FinalAnswer|> without providing a final answer at the end, you can ignore this or modify the prompt format.
this is out flagship model, with top-tier reasoning, rivaling gemini-flash-exp-2.0-thinking and o1 mini. results are overall similar to both of them, we are not comparing to qwq as it has much longer results which waste tokens.

🧀 Uploaded model

Developed by: Pinkstack
License: MIT
Finetuned from model : Pinkstack/PARM-V1-phi-4-4k-CoT-pytorch

This Phi-4 model was trained with Unsloth and Huggingface's TRL library.