Instructions to use AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml with Transformers:

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

pipe = pipeline("text-generation", model="AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml", 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("AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml", 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 AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml

SGLang

How to use AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml 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 "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml" \
    --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": "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml",
		"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 "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml" \
        --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": "AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml with Docker Model Runner:
```
docker model run hf.co/AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml
```

See axolotl config

axolotl version: 0.4.0

base_model: AlekseyKorshuk/ultrachat-phi-2-sft-chatml
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AlekseyKorshuk/evol-codealpaca-v1-sft
    type: sharegpt
    conversation: chatml

dataset_prepared_path:
val_set_size: 0
output_dir: ./output

sequence_len: 2048
sample_packing: false
pad_to_sequence_len:

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: ui-thesis
wandb_entity:
wandb_watch:
wandb_name: ultrachat-evolcode-phi-2-sft-chatml
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 2e-5
warmup_ratio: 0.1
weight_decay: 0.1

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

#bf16: false
#fp16: false
#tf32: false
#float16: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true


evals_per_epoch: 0
eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128
eval_sample_packing: false

chat_template: chatml
saves_per_epoch: 5
save_total_limit: 1
seed: 42
debug:
deepspeed:

fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true

ultrachat-evolcode-phi-2-sft-chatml

This model is a fine-tuned version of AlekseyKorshuk/ultrachat-phi-2-sft-chatml on the None dataset.

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 7
num_epochs: 1

Training results

Framework versions

Transformers 4.37.0
Pytorch 2.1.2+cu118
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 8

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for AlekseyKorshuk/ultrachat-evolcode-phi-2-sft-chatml

Base model

microsoft/phi-2

Finetuned

AlekseyKorshuk/ultrachat-phi-2-sft-chatml

Finetuned

(2)

this model