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

Libraries

How to use shahdaboelfotouh/finetunedmodelll with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shahdaboelfotouh/finetunedmodelll")
model = AutoModelForCausalLM.from_pretrained("shahdaboelfotouh/finetunedmodelll", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shahdaboelfotouh/finetunedmodelll with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/shahdaboelfotouh/finetunedmodelll

SGLang

How to use shahdaboelfotouh/finetunedmodelll 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 "shahdaboelfotouh/finetunedmodelll" \
    --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": "shahdaboelfotouh/finetunedmodelll",
		"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 "shahdaboelfotouh/finetunedmodelll" \
        --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": "shahdaboelfotouh/finetunedmodelll",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

finetunedmodelll — SmolLM2-360M Fine-Tuned on Deepthink-Reasoning

finetunedmodelll is a fine-tuned version of HuggingFaceTB/SmolLM2-360M, trained on the prithivMLmods/Deepthink-Reasoning dataset.

The goal of this model is to generate step-by-step reasoning and final answers for tasks such as arithmetic, logic, and basic conceptual questions. It is formatted as a chat model using the SmolLM2 chat template and trained with supervised fine-tuning (SFT).

This model is intended for educational and experimental uses, not for high-stakes decision-making.

Features

Base model: SmolLM2-360M (small, fast, and lightweight).
Fine-tuned on Deepthink-Reasoning (instruction–response pairs with detailed reasoning).
Uses chat-style formatting via tokenizer.apply_chat_template with user and assistant roles.
Produces:
- Step-by-step reasoning.
- A final concise answer at the end.
Suitable for:
- Simple math and arithmetic reasoning.
- Logic-style questions.
- Educational demonstrations of chain-of-thought.

Intended Use

Not Recommended

Medical, legal, or financial advice.
High-stakes or real-world decision-making.
Safety-critical applications.
Factual tasks where reliability is crucial.

This is an experimental model trained on a reasoning dataset and is not designed for reliable factual knowledge or domain-specific professional use.

Training Data

Dataset: prithivMLmods/Deepthink-Reasoning
Format:
- Columns: at least prompt and response.
- Each example is converted to a chat format:
  - {"role": "user", "content": prompt}
  - {"role": "assistant", "content": response}

The notebook uses:

texts = [
    tokenizer.apply_chat_template(
        [
            {"role": "user", "content": p},
            {"role": "assistant", "content": r},
        ],
        tokenize=False
    )
    for p, r in zip(prompts, responses)
]

Downloads last month: 6

Safetensors

Model size

0.4B params

Tensor type

BF16

Model tree for shahdaboelfotouh/finetunedmodelll

Base model

HuggingFaceTB/SmolLM2-360M

Finetuned

(113)

this model