Instructions to use rasyosef/phi-1_5-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rasyosef/phi-1_5-sft with Transformers:

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

pipe = pipeline("text-generation", model="rasyosef/phi-1_5-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rasyosef/phi-1_5-sft")
model = AutoModelForCausalLM.from_pretrained("rasyosef/phi-1_5-sft")
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

vLLM

How to use rasyosef/phi-1_5-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rasyosef/phi-1_5-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rasyosef/phi-1_5-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rasyosef/phi-1_5-sft

SGLang

How to use rasyosef/phi-1_5-sft 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 "rasyosef/phi-1_5-sft" \
    --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": "rasyosef/phi-1_5-sft",
		"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 "rasyosef/phi-1_5-sft" \
        --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": "rasyosef/phi-1_5-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rasyosef/phi-1_5-sft with Docker Model Runner:
```
docker model run hf.co/rasyosef/phi-1_5-sft
```

phi-1_5-sft-openhermes-v2

This model is a fine-tuned version of microsoft/phi-1_5 on the generator dataset. It achieves the following results on the evaluation set:

Loss: 1.1750

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: 8e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
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: 500
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.7865	0.0831	275	1.4033
1.3614	0.1663	550	1.3218
1.2986	0.2494	825	1.2788
1.2667	0.3325	1100	1.2531
1.2405	0.4157	1375	1.2376
1.2239	0.4988	1650	1.2237
1.2078	0.5819	1925	1.2122
1.2114	0.6651	2200	1.2005
1.2028	0.7482	2475	1.1915
1.173	0.8313	2750	1.1833
1.1782	0.9144	3025	1.1776
1.1805	0.9976	3300	1.1720
1.0112	1.0807	3575	1.1817
0.9988	1.1638	3850	1.1791
0.9919	1.2470	4125	1.1786
0.9886	1.3301	4400	1.1768
0.9904	1.4132	4675	1.1763
1.001	1.4964	4950	1.1756
0.9979	1.5795	5225	1.1751
0.9858	1.6626	5500	1.1750
0.9975	1.7458	5775	1.1750
0.9924	1.8289	6050	1.1750
0.9978	1.9120	6325	1.1750
0.9892	1.9952	6600	1.1750