Instructions to use KrisMinchev/train-bioR-concat-gen4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KrisMinchev/train-bioR-concat-gen4 with Transformers:

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

pipe = pipeline("text-generation", model="KrisMinchev/train-bioR-concat-gen4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KrisMinchev/train-bioR-concat-gen4")
model = AutoModelForCausalLM.from_pretrained("KrisMinchev/train-bioR-concat-gen4")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KrisMinchev/train-bioR-concat-gen4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KrisMinchev/train-bioR-concat-gen4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KrisMinchev/train-bioR-concat-gen4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KrisMinchev/train-bioR-concat-gen4

SGLang

How to use KrisMinchev/train-bioR-concat-gen4 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 "KrisMinchev/train-bioR-concat-gen4" \
    --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": "KrisMinchev/train-bioR-concat-gen4",
		"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 "KrisMinchev/train-bioR-concat-gen4" \
        --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": "KrisMinchev/train-bioR-concat-gen4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KrisMinchev/train-bioR-concat-gen4 with Docker Model Runner:
```
docker model run hf.co/KrisMinchev/train-bioR-concat-gen4
```

train-bioR-concat-gen4

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

Loss: 1.5219

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: 0.001
train_batch_size: 12
eval_batch_size: 12
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 96
total_eval_batch_size: 96
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
training_steps: 41972
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.7002	0.2382	10000	1.6141
0.6777	0.4765	20000	1.5702
0.6585	0.7147	30000	1.5310
0.6584	0.9530	40000	1.5219

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 1

Safetensors

Model size

0.4B params

Tensor type

F32