Instructions to use AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA with Transformers:

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

pipe = pipeline("text-generation", model="AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA", 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("AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA", 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 AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA

SGLang

How to use AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA 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 "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA" \
    --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": "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA",
		"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 "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA" \
        --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": "AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA with Docker Model Runner:
```
docker model run hf.co/AdnanRiaz107/CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA
```

CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA / README.md

AdnanRiaz107

CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA

88f9551 verified over 2 years ago

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---
license: mit
base_model: AdnanRiaz107/CodePhi-3-mini-4k-instruct-python
tags:
- generated_from_trainer
model-index:
- name: CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA
  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. -->

# CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA

This model is a fine-tuned version of [AdnanRiaz107/CodePhi-3-mini-4k-instruct-python](https://huggingface.co/AdnanRiaz107/CodePhi-3-mini-4k-instruct-python) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5795

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1200

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6559        | 0.1667 | 200  | 0.6520          |
| 0.6709        | 0.3333 | 400  | 0.6225          |
| 0.5701        | 0.5    | 600  | 0.5980          |
| 0.5371        | 0.6667 | 800  | 0.5818          |
| 0.4926        | 0.8333 | 1000 | 0.5789          |
| 0.5058        | 1.0    | 1200 | 0.5795          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

	---
	license: mit
	base_model: AdnanRiaz107/CodePhi-3-mini-4k-instruct-python
	tags:
	- generated_from_trainer
	model-index:
	- name: CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA
	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. -->

	# CodePhi-3-mini-4k-instruct-pythonAPPSWOLORA

	This model is a fine-tuned version of [AdnanRiaz107/CodePhi-3-mini-4k-instruct-python](https://huggingface.co/AdnanRiaz107/CodePhi-3-mini-4k-instruct-python) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5795

	## 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: 5e-05
	- train_batch_size: 1
	- eval_batch_size: 1
	- seed: 42
	- gradient_accumulation_steps: 16
	- total_train_batch_size: 16
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- training_steps: 1200

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|
	\| 0.6559 \| 0.1667 \| 200 \| 0.6520 \|
	\| 0.6709 \| 0.3333 \| 400 \| 0.6225 \|
	\| 0.5701 \| 0.5 \| 600 \| 0.5980 \|
	\| 0.5371 \| 0.6667 \| 800 \| 0.5818 \|
	\| 0.4926 \| 0.8333 \| 1000 \| 0.5789 \|
	\| 0.5058 \| 1.0 \| 1200 \| 0.5795 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1