MyModel2

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1089

Model description

This is a fine-tuned model available in both SafeTensors and GGUF formats. The GGUF version allows efficient inference with tools like llama.cpp and ctransformers.

Intended uses & limitations

This model can be used for various natural language processing tasks. However, it may have limitations based on the dataset and fine-tuning constraints.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 5
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.9498	0.2693	500	0.6119
0.6245	0.5385	1000	0.5831
0.5931	0.8078	1500	0.5462
0.561	1.0770	2000	0.5148
0.5312	1.3463	2500	0.4750
0.523	1.6155	3000	0.4421
0.5121	1.8848	3500	0.4096
0.4059	2.1540	4000	0.3263
0.3559	2.4233	4500	0.2780
0.3409	2.6925	5000	0.2367
0.3352	2.9618	5500	0.1973
0.1918	3.2310	6000	0.1652
0.1826	3.5003	6500	0.1507
0.1762	3.7695	7000	0.1360
0.168	4.0388	7500	0.1232
0.1186	4.3080	8000	0.1193
0.1227	4.5773	8500	0.1134
0.1273	4.8465	9000	0.1089

Inference

This model supports inference via GGUF using llama.cpp or ctransformers.

Using llama.cpp (CLI)

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m first.gguf -p "Hello, how are you?"

Using ctransformers (Python)

from ctransformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "your_username/your_model_repo",
    model_file="first.gguf",
    model_type="llama"
)

output = model("Hello, how are you?")
print(output)

Framework versions

• Transformers 4.48.2
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0