Instructions to use gardner/TinyLlama-1.1B-DPO-Function-Calling-3T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gardner/TinyLlama-1.1B-DPO-Function-Calling-3T with Transformers:

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

pipe = pipeline("text-generation", model="gardner/TinyLlama-1.1B-DPO-Function-Calling-3T")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gardner/TinyLlama-1.1B-DPO-Function-Calling-3T")
model = AutoModelForCausalLM.from_pretrained("gardner/TinyLlama-1.1B-DPO-Function-Calling-3T")
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 gardner/TinyLlama-1.1B-DPO-Function-Calling-3T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gardner/TinyLlama-1.1B-DPO-Function-Calling-3T

SGLang

How to use gardner/TinyLlama-1.1B-DPO-Function-Calling-3T 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 "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T" \
    --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": "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T",
		"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 "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T" \
        --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": "gardner/TinyLlama-1.1B-DPO-Function-Calling-3T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gardner/TinyLlama-1.1B-DPO-Function-Calling-3T with Docker Model Runner:
```
docker model run hf.co/gardner/TinyLlama-1.1B-DPO-Function-Calling-3T
```

TinyLlama-1.1B-DPO-Function-Calling-3T

This model is a DPO fine tune of gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T which itself was trained on:

The model scores unusually high on GSM8K which indicates the glaive function calling dataset may introduce data contamination.

See axolotl config

axolotl version: 0.4.0

base_model: gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
chat_template: chatml

is_llama_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: argilla/distilabel-intel-orca-dpo-pairs
    split: train
    type: chatml.gardner

dataset_prepared_path: ./dsprepare/argilla/distilabel-intel-orca-dpo-pairs
val_set_size: 0.05
output_dir: ./TinyLlama-1.1B-DPO-Function-Calling-3T

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false

adapter: lora
lora_model_dir:

lora_r: 256
lora_alpha: 128
lora_dropout: 0.05
lora_target_linear: true
lora_modules_to_save:
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj


wandb_project: tinyllama
wandb_entity: gardner
wandb_name: tinyllama-distilabel-intel-orca-dpo-pairs

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
adam_beta2: 0.95
adam_epsilion: 0.00001
lr_scheduler: linear
learning_rate: 1.414e-5

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

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

warmup_steps: 10
eval_steps:
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 45
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
save_safetensors: true

dataloader_num_workers: 16
dataloader_pin_memory: true

TinyLlama-1.1B-DPO-Function-Calling-3T

This model is a fine-tuned version of gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T on the None dataset.

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1.414e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 19289

Training results

Framework versions

Transformers 4.37.0
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

F32

Model tree for gardner/TinyLlama-1.1B-DPO-Function-Calling-3T

Base model

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Adapter

gardner/TinyLlama-1.1B-SlimOrca-Function-Calling-3T

Quantized

(1)

this model

gardner
/

TinyLlama-1.1B-DPO-Function-Calling-3T

TinyLlama-1.1B-DPO-Function-Calling-3T

TinyLlama-1.1B-DPO-Function-Calling-3T

Training hyperparameters

Training results

Framework versions

Model tree for gardner/TinyLlama-1.1B-DPO-Function-Calling-3T

Dataset used to train gardner/TinyLlama-1.1B-DPO-Function-Calling-3T