theprint / ComputerScience-v1-2B-GGUF

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Total runs: 1.1K
24-hour runs: 0
7-day runs: 340
30-day runs: 1.1K
Model's Last Updated: September 12 2026

Introduction of ComputerScience-v1-2B-GGUF

Model Details of ComputerScience-v1-2B-GGUF

ComputerScience-v1-2B (GGUF)

A fine-tuned version of unsloth/Qwen3.5-2B trained on ComputerScience ML AI Chatml 7.0k 110926 train data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the ComputerScience ML AI Chatml 7.0k 110926 train dataset. Expect improved performance on tasks similar to those represented in the training data.

Model Details
Property Value
Base model unsloth/Qwen3.5-2B
Training data data/ComputerScience-ML-AI-Chatml-7.0k-110926_train.json
Fine-tuning epochs 2
Fine-tuning date 2026-09-12
Fine-tuning method LoRA (merged to full 16-bit)
Training Hyperparameters
LoRA
Parameter Value
r 4
alpha 8
dropout 0.02
target_modules ['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
Training
Parameter Value
learning_rate 0.0005
batch_size 4
gradient_accumulation_steps 1
warmup_ratio 0.0
max_seq_length 2048
quantization none
GGUF Files

These quantized GGUF files can be used directly with llama.cpp , Ollama , LM Studio , and other compatible runtimes.

File Description
ComputerScience-v1-2B-GGUF-BF16.gguf BF16
ComputerScience-v1-2B-GGUF-Q8_0.gguf 8-bit — near-lossless, larger file
ComputerScience-v1-2B-GGUF-Q6_K.gguf 6-bit — high quality
ComputerScience-v1-2B-GGUF-Q5_K_M.gguf 5-bit medium — good quality/size balance
ComputerScience-v1-2B-GGUF-Q5_K_S.gguf Q5_K_S
ComputerScience-v1-2B-GGUF-Q4_K_M.gguf 4-bit medium — recommended for most use cases
ComputerScience-v1-2B-GGUF-Q4_K_S.gguf Q4_K_S
ComputerScience-v1-2B-GGUF-Q3_K_L.gguf Q3_K_L
ComputerScience-v1-2B-GGUF-Q3_K_M.gguf Q3_K_M
ComputerScience-v1-2B-GGUF-Q3_K_S.gguf Q3_K_S
ComputerScience-v1-2B-GGUF-Q2_K.gguf 2-bit — smallest size, lowest quality
ComputerScience-v1-2B-GGUF-IQ4_XS.gguf IQ4_XS
ComputerScience-v1-2B-GGUF-IQ4_NL.gguf IQ4_NL
ComputerScience-v1-2B-GGUF-TQ2_0.gguf TQ2_0

Generated by Auto-SFT — automated LoRA fine-tuning with hyperparameter search.

Runs of theprint ComputerScience-v1-2B-GGUF on huggingface.co

1.1K
Total runs
0
24-hour runs
10
3-day runs
340
7-day runs
1.1K
30-day runs

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ComputerScience-v1-2B-GGUF huggingface.co is an AI model on huggingface.co that provides ComputerScience-v1-2B-GGUF's model effect (), which can be used instantly with this theprint ComputerScience-v1-2B-GGUF model. huggingface.co supports a free trial of the ComputerScience-v1-2B-GGUF model, and also provides paid use of the ComputerScience-v1-2B-GGUF. Support call ComputerScience-v1-2B-GGUF model through api, including Node.js, Python, http.

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https://huggingface.co/theprint/ComputerScience-v1-2B-GGUF

ComputerScience-v1-2B-GGUF install

ComputerScience-v1-2B-GGUF is an open source model from GitHub that offers a free installation service, and any user can find ComputerScience-v1-2B-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of ComputerScience-v1-2B-GGUF install, users can directly use ComputerScience-v1-2B-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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Total runs: 66
Run Growth: -2
Growth Rate: -3.03%
Updated:November 03 2024