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---
license: apache-2.0
datasets:
- huihui-ai/FineQwQ-142k
base_model:
- huihui-ai/Llama-3.2-3B-Instruct-abliterated
tags:
- llama3.2
- abliterated
- uncensored
library_name: transformers
pipeline_tag: text-generation
language:
- en
---
# MicroThinker-3B-Preview
MicroThinker-3B-Preview, a new model fine-tuned from the [huihui-ai/Llama-3.2-3B-Instruct-abliterated](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated) model, focused on advancing AI reasoning capabilities.
## Use with ollama
You can use [huihui_ai/microthinker](https://ollama.com/huihui_ai/microthinker) directly
```
ollama run huihui_ai/microthinker:3b
```
## Training Details
This is just a test, but the performance is quite good.
Now, I'll introduce the test environment.
The model was trained using 1 RTX 4090 GPU(24GB) .
The fine-tuning process used 142k from the FineQwQ-142k dataset, max_length(tokens) 21710, quant_bits 4.
The [SFT (Supervised Fine-Tuning)](https://github.com/modelscope/ms-swift) process is divided into several steps, and no code needs to be written.
1. Create the environment.
```
conda create -yn ms-swift python=3.11
conda activate ms-swift
git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e .
cd ..
```
2. Download the model and dataset.
```
huggingface-cli download huihui-ai/Llama-3.2-3B-Instruct-abliterated --local-dir ./huihui-ai/Llama-3.2-3B-Instruct-abliterated
huggingface-cli download --repo-type dataset huihui-ai/FineQwQ-142k --local-dir ./data/FineQwQ-142k
```
3. Used only the huihui-ai/FineQwQ-142k, Trained for 1 epoch:
```
swift sft --model huihui-ai/Llama-3.2-3B-Instruct-abliterated --model_type llama3_2 --train_type lora --dataset "data/FineQwQ-142k/FineQwQ-142k.jsonl" --num_train_epochs 1 --per_device_train_batch_size 1 --per_device_eval_batch_size 1 --max_length 21710 --quant_bits 4 --bnb_4bit_compute_dtype bfloat16 --bnb_4bit_quant_storage bfloat16 --lora_rank 8 --lora_alpha 32 --gradient_checkpointing true --weight_decay 0.1 --learning_rate 1e-4 --gradient_accumulation_steps 16 --eval_steps 100 --save_steps 100 --logging_steps 20 --system "You are a helpful assistant. You should think step-by-step." --output_dir output/MicroThinker-3B-Preview/lora/sft --model_author "huihui-ai" --model_name "MicroThinker-3B-Preview"
```
4. Save the final fine-tuned model. After you're done, input `exit` to exit.
Replace the directories below with specific ones.
```
swift infer --model huihui-ai/Llama-3.2-3B-Instruct-abliterated --adapters output/Llama-3.2-3B-Instruct-abliterated/lora/sft/v0-20250106-193759/checkpoint-8786 --stream true --merge_lora true
```
This should create a new model directory: `checkpoint-8786-merged`, Rename the directory to `MicroThinker-3B-Preview`, Copy or move this directory to the `huihui` directory.
5. Perform inference on the final fine-tuned model.
```
swift infer --model huihui/MicroThinker-3B-Preview --stream true --infer_backend pt --max_new_tokens 8192
```
6. Test examples.
```
How many 'r' characters are there in the word "strawberry"?
``` |