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 16, 2025

Commit

f931cf8

verified ·

1 Parent(s): 239ceb8

Model save

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README.md +11 -3

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ should probably proofread and complete it, then remove this comment. -->
 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
@@ -47,14 +47,22 @@ The following hyperparameters were used during training:
 - 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

 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: 1.5572
 ## Model description
 - 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: 5.0
 - mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch  | Step | Validation Loss |
 |:-------------:|:------:|:----:|:---------------:|
+| 1.533         | 0.5382 | 200  | 1.4790          |
+| 1.3687        | 1.0753 | 400  | 1.4093          |
+| 1.2869        | 1.6135 | 600  | 1.3642          |
+| 0.9743        | 2.1507 | 800  | 1.3826          |
+| 1.0377        | 2.6889 | 1000 | 1.3527          |
+| 0.7669        | 3.2260 | 1200 | 1.4627          |
+| 0.7772        | 3.7642 | 1400 | 1.4342          |
+| 0.5632        | 4.3014 | 1600 | 1.5544          |
+| 0.5757        | 4.8396 | 1800 | 1.5572          |
 ### Framework versions