Instructions to use FayElhassan/deepseek_Pregnancy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FayElhassan/deepseek_Pregnancy with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-llm-7b-chat")
model = PeftModel.from_pretrained(base_model, "FayElhassan/deepseek_Pregnancy")

Transformers

How to use FayElhassan/deepseek_Pregnancy with Transformers:

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

pipe = pipeline("text-generation", model="FayElhassan/deepseek_Pregnancy")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FayElhassan/deepseek_Pregnancy", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FayElhassan/deepseek_Pregnancy with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FayElhassan/deepseek_Pregnancy"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FayElhassan/deepseek_Pregnancy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FayElhassan/deepseek_Pregnancy

SGLang

How to use FayElhassan/deepseek_Pregnancy 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 "FayElhassan/deepseek_Pregnancy" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FayElhassan/deepseek_Pregnancy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "FayElhassan/deepseek_Pregnancy" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FayElhassan/deepseek_Pregnancy",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FayElhassan/deepseek_Pregnancy with Docker Model Runner:
```
docker model run hf.co/FayElhassan/deepseek_Pregnancy
```

FayElhassan commited on Dec 15, 2025

Commit

155df97

verified ·

1 Parent(s): 991b988

Model save

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README.md +47 -39

README.md CHANGED Viewed

@@ -1,58 +1,66 @@
 ---
 base_model: deepseek-ai/deepseek-llm-7b-chat
-library_name: transformers
-model_name: deepseek_Pregnancy
 tags:
-- generated_from_trainer
-- trl
-- sft
-licence: license
 ---
-# Model Card for deepseek_Pregnancy
-This model is a fine-tuned version of [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-## Quick start
-```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="FayElhassan/deepseek_Pregnancy", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-## Training procedure
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/elhassan-aimsammi/medical-llm-pregnancy/runs/am3sv4ry)
-This model was trained with SFT.
-### Framework versions
-- TRL: 0.26.0
-- Transformers: 4.56.1
-- Pytorch: 2.8.0
-- Datasets: 4.1.0
-- Tokenizers: 0.22.0
-## Citations
-Cite TRL as:
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```

 ---
+library_name: peft
+license: other
 base_model: deepseek-ai/deepseek-llm-7b-chat
 tags:
+- base_model:adapter:deepseek-ai/deepseek-llm-7b-chat
+- lora
+- transformers
+pipeline_tag: text-generation
+model-index:
+- name: deepseek_Pregnancy
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# deepseek_Pregnancy
+This model is a fine-tuned version of [deepseek-ai/deepseek-llm-7b-chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.4711
+## 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: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 1.0
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 1.5499        | 0.5398 | 200  | 1.4711          |
+### Framework versions
+- PEFT 0.17.1
+- Transformers 4.56.1
+- Pytorch 2.8.0+cu128
+- Datasets 4.1.0
+- Tokenizers 0.22.0