Instructions to use SAMLL5AAAAA/MentalSeek-Dx-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SAMLL5AAAAA/MentalSeek-Dx-7B with Transformers:

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

pipe = pipeline("text-generation", model="SAMLL5AAAAA/MentalSeek-Dx-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SAMLL5AAAAA/MentalSeek-Dx-7B")
model = AutoModelForCausalLM.from_pretrained("SAMLL5AAAAA/MentalSeek-Dx-7B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SAMLL5AAAAA/MentalSeek-Dx-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SAMLL5AAAAA/MentalSeek-Dx-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SAMLL5AAAAA/MentalSeek-Dx-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SAMLL5AAAAA/MentalSeek-Dx-7B

SGLang

How to use SAMLL5AAAAA/MentalSeek-Dx-7B 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 "SAMLL5AAAAA/MentalSeek-Dx-7B" \
    --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": "SAMLL5AAAAA/MentalSeek-Dx-7B",
		"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 "SAMLL5AAAAA/MentalSeek-Dx-7B" \
        --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": "SAMLL5AAAAA/MentalSeek-Dx-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SAMLL5AAAAA/MentalSeek-Dx-7B with Docker Model Runner:
```
docker model run hf.co/SAMLL5AAAAA/MentalSeek-Dx-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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train_2025-12-13-17-23-09

This model is a fine-tuned version of /data/testmllm/models/Qwen/Qwen2.5-7B-Instruct on the train-data-15000-sft dataset.

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: 5e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 32
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 70
num_epochs: 3.0

Training results

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 88

Safetensors

Model size

333k params

Tensor type

BF16

Model tree for SAMLL5AAAAA/MentalSeek-Dx-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(2937)

this model

Quantizations

1 model