Instructions to use Barghav777/phi3-lab-report-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Barghav777/phi3-lab-report-coder with Transformers:

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

pipe = pipeline("text-generation", model="Barghav777/phi3-lab-report-coder", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Barghav777/phi3-lab-report-coder", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Barghav777/phi3-lab-report-coder", trust_remote_code=True, 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 Barghav777/phi3-lab-report-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Barghav777/phi3-lab-report-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Barghav777/phi3-lab-report-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Barghav777/phi3-lab-report-coder

SGLang

How to use Barghav777/phi3-lab-report-coder 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 "Barghav777/phi3-lab-report-coder" \
    --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": "Barghav777/phi3-lab-report-coder",
		"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 "Barghav777/phi3-lab-report-coder" \
        --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": "Barghav777/phi3-lab-report-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Barghav777/phi3-lab-report-coder with Docker Model Runner:
```
docker model run hf.co/Barghav777/phi3-lab-report-coder
```

Barghav777 commited on Sep 17, 2025

Commit

62fa8c0

verified ·

1 Parent(s): fcad89f

Update README.md

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README.md CHANGED Viewed

@@ -200,13 +200,19 @@ _ = model.generate(**inputs, max_new_tokens=400, temperature=0.2, do_sample=Fals
 ### Compute Infrastructure
 - **Hardware:** Colab GPU (T4) + CPU RAM
 - **Software:**
-  - `transformers`, `trl`, `peft`, `bitsandbytes`, `datasets`, `accelerate`, `torch`
-  - Notebook: `Untitled64 (1).ipynb`
 ---
 ## Citation
-If you write about this model, please cite the base model and your repository. (Add BibTeX here if/when available.)
 ---

 ### Compute Infrastructure
 - **Hardware:** Colab GPU (T4) + CPU RAM
 - **Software:**
+  - `transformers`, `trl`, `peft`, `bitsandbytes`, `datasets`, `accelerate`, `torch`
 ---
 ## Citation
+@article{abdin2024phi3,
+  title   = {Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone},
+  author  = {Abdin, Marah and others},
+  journal = {arXiv preprint arXiv:2404.14219},
+  year    = {2024},
+  doi     = {10.48550/arXiv.2404.14219},
+  url     = {https://arxiv.org/abs/2404.14219}
+}
 ---