Instructions to use Vipul231/phi3-lora-scriptgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vipul231/phi3-lora-scriptgen with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/phi-3-mini-4k-instruct")
model = PeftModel.from_pretrained(base_model, "Vipul231/phi3-lora-scriptgen")

Transformers

How to use Vipul231/phi3-lora-scriptgen with Transformers:

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

pipe = pipeline("text-generation", model="Vipul231/phi3-lora-scriptgen", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vipul231/phi3-lora-scriptgen", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Vipul231/phi3-lora-scriptgen", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vipul231/phi3-lora-scriptgen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vipul231/phi3-lora-scriptgen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vipul231/phi3-lora-scriptgen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vipul231/phi3-lora-scriptgen

SGLang

How to use Vipul231/phi3-lora-scriptgen 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 "Vipul231/phi3-lora-scriptgen" \
    --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": "Vipul231/phi3-lora-scriptgen",
		"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 "Vipul231/phi3-lora-scriptgen" \
        --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": "Vipul231/phi3-lora-scriptgen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vipul231/phi3-lora-scriptgen with Docker Model Runner:
```
docker model run hf.co/Vipul231/phi3-lora-scriptgen
```

Vipul231 commited on Jul 6, 2025

Commit

451ad48

verified ·

1 Parent(s): 1a93215

Upload folder using huggingface_hub

Browse files

Files changed (4) hide show

README.md +12 -4
adapter_config.json +38 -0
adapter_model.safetensors +3 -0
tokenizer.json +16 -2

README.md CHANGED Viewed

@@ -1,6 +1,11 @@
 ---
-library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
@@ -15,7 +20,7 @@ tags: []
 <!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
@@ -196,4 +201,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 ## Model Card Contact
-[More Information Needed]

 ---
+base_model: microsoft/phi-3-mini-4k-instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:microsoft/phi-3-mini-4k-instruct
+- lora
+- transformers
 ---
 # Model Card for Model ID
 <!-- Provide a longer summary of what this model is. -->
 - **Developed by:** [More Information Needed]
 - **Funded by [optional]:** [More Information Needed]
 ## Model Card Contact
+[More Information Needed]
+### Framework versions
+- PEFT 0.16.0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,38 @@

+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "microsoft/phi-3-mini-4k-instruct",
+  "bias": "none",
+  "corda_config": null,
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+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
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+  "target_modules": [
+    "o_proj",
+    "qkv_proj",
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+    "gate_up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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