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Model: Pinkstack/Superthoughts-lite-1.8B-experimental-o1-GGUF Source: Original Platform
2026-09-19 01:34:16 +08:00
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+---
+base_model: Pinkstack/Superthoughts-lite-1.8B-experimental-o1
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+- sft
+- code
+- superthoughts
+- cot
+- reasoning
+- gguf
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+new_version: Pinkstack/Superthoughts-lite-v1-GGUF
+---
+![superthoughtslight.png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/2LuPB_ZPCGni3-PyCkL0-.png)
+
+# Information
+Advanced, high-quality and lite reasoning for a tiny size that you can run locally in Q8 on your phone! 😲
+
+⚠️This is an experimental version: it may not always answer your question properly or correctly. currently reasoning may not always work on long conversations, as we've trained it on single turn conversations only.
+SmolLM2-1.7B-Instruct on an advanced reasoning pattern dataset (half synthetic, half written manually by us.) to create this model. Supposed to output like this:
+```
+<|im_start|>user
+What are you<|im_end|>
+<|im_start|>assistant
+<think>
+Alright, the user just asked 'What are you', meaning they want to know who I am. I think my name is Superthoughts (lite version), created by Pinkstack on January 2025. I'm ready to answer their question.
+</think>
+Welcome! I'm Superthoughts (lite) created by Pinkstack in January 2025. Ready to help you with whatever you need!<|im_end|>
+```
+# Which quant is right for you?
+- ***Q4_k_m:*** This quant *can* be used on most devices, quality is acceptable but reasoning quality is low.
+- ***Q6_k:*** This quant is right in the middle, quality is better than q4_k_m but reasoning is still more limited than Q8.
+- ***Q8_0:*** **RECOMMENDED** This quant yields very high quality results, good reasoning, good answers at a fast speed, on a Snapdragon 8 Gen 2 with 16 GB's of ram, it runs on 13 tokens per minute on average, see examples below.
+- ***F16:*** Maximum quality GGUF quant, not needed for most tasks, results very similar to Q8_0.
+
+# Examples:
+all responses below generated with no system prompt, 400 maximum tokens and a temperature of 0.7 (not recommended, 0.3 - 0.5 is better):
+Generated inside the android application, Pocketpal via GGUF Q8, using the model's prompt format.
+1)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/wh33o-vjxIePfPqoN3q1z.png)
+2)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/Y8optw73kTgqMnZKj3wKj.png)
+3)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/6lywy3IYEIgzPnUIJ5RvF.png)
+4)
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/0K2rR9osmT20JrDvZuptV.png)
+
+# Uploaded  model
+
+- **Developed by:** Pinkstack
+- **License:** apache-2.0
+- **Finetuned from model :** HuggingFaceTB/SmolLM2-1.7B-Instruct
+
+This smollm2 model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.