Instructions to use anilguleroglu/cllm-1.0.0_old with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anilguleroglu/cllm-1.0.0_old with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="anilguleroglu/cllm-1.0.0_old")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anilguleroglu/cllm-1.0.0_old")
model = AutoModelForCausalLM.from_pretrained("anilguleroglu/cllm-1.0.0_old", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anilguleroglu/cllm-1.0.0_old with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anilguleroglu/cllm-1.0.0_old"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguleroglu/cllm-1.0.0_old",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anilguleroglu/cllm-1.0.0_old

SGLang

How to use anilguleroglu/cllm-1.0.0_old with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "anilguleroglu/cllm-1.0.0_old" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguleroglu/cllm-1.0.0_old",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "anilguleroglu/cllm-1.0.0_old" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anilguleroglu/cllm-1.0.0_old",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anilguleroglu/cllm-1.0.0_old with Docker Model Runner:
```
docker model run hf.co/anilguleroglu/cllm-1.0.0_old
```

cllm-1.0.0

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.7403

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0003
train_batch_size: 4
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 8
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 50
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0044	2000	3.8105
4.2179	0.0089	4000	3.4461
4.2179	0.0133	6000	3.2612
3.2341	0.0178	8000	3.1610
3.2341	0.0222	10000	3.0834
3.0422	0.0267	12000	3.0260
3.0422	0.0311	14000	2.9872
2.9347	0.0356	16000	2.9444
2.9347	0.0400	18000	2.9090
2.874	0.0445	20000	2.8854
2.874	0.0489	22000	2.8585
2.8204	0.0534	24000	2.8405
2.8204	0.0578	26000	2.8245
2.7825	0.0622	28000	2.8106
2.7825	0.0667	30000	2.7993
2.7555	0.0711	32000	2.7867
2.7555	0.0756	34000	2.7738
2.7285	0.0800	36000	2.7700
2.7285	0.0845	38000	2.7597
2.7179	0.0889	40000	2.7591
2.7179	0.0934	42000	2.7488
2.7119	0.0978	44000	2.7512
2.7119	0.1023	46000	2.7487
2.7043	0.1067	48000	2.7436
2.7043	0.1111	50000	2.7422
2.7013	0.1156	52000	2.7435
2.7013	0.1200	54000	2.7388
2.7029	0.1245	56000	2.7380
2.7029	0.1289	58000	2.7403

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.3.2
Tokenizers 0.21.1

Downloads last month: 6

Safetensors

Model size

2B params

Tensor type

BF16