Instructions to use msaavedra1234/phi3_parise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use msaavedra1234/phi3_parise with Transformers:

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

pipe = pipeline("text-generation", model="msaavedra1234/phi3_parise", 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("msaavedra1234/phi3_parise", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("msaavedra1234/phi3_parise", trust_remote_code=True)
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 msaavedra1234/phi3_parise with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "msaavedra1234/phi3_parise"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "msaavedra1234/phi3_parise",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/msaavedra1234/phi3_parise

SGLang

How to use msaavedra1234/phi3_parise 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 "msaavedra1234/phi3_parise" \
    --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": "msaavedra1234/phi3_parise",
		"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 "msaavedra1234/phi3_parise" \
        --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": "msaavedra1234/phi3_parise",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use msaavedra1234/phi3_parise with Docker Model Runner:
```
docker model run hf.co/msaavedra1234/phi3_parise
```

See axolotl config

axolotl version: 0.4.1

base_model: microsoft/Phi-3-mini-4k-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: dataset.json
    ds_type: json
    type: completion

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./phi3-out

sequence_len: 4096
sample_packing: false
#pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch
# adam_beta2: 0.95
# adam_epsilon: 0.00001
# max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.0002 # 0.000003 #0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

# gradient_checkpointing: true
# gradient_checkpointing_kwargs:
#   use_reentrant: True
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

#warmup_steps: 100
#evals_per_epoch: 4
# saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
#resize_token_embeddings_to_32x: true
special_tokens:
  pad_token: "<|endoftext|>"
  eos_token: "<|end|>"

phi3-out

This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.8809

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
0.4023	1.0	7628	1.4132
0.1342	2.0	15256	1.8809

Framework versions

Transformers 4.42.3
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

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microsoft/Phi-3-mini-4k-instruct

Finetuned

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