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

Libraries

How to use msaavedra1234/tiny_t with Transformers:

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

pipe = pipeline("text-generation", model="msaavedra1234/tiny_t")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("msaavedra1234/tiny_t")
model = AutoModelForCausalLM.from_pretrained("msaavedra1234/tiny_t")
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]:]))

llama-cpp-python

How to use msaavedra1234/tiny_t with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="msaavedra1234/tiny_t",
	filename="ggml-model-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use msaavedra1234/tiny_t with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf msaavedra1234/tiny_t:F16
# Run inference directly in the terminal:
llama-cli -hf msaavedra1234/tiny_t:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf msaavedra1234/tiny_t:F16
# Run inference directly in the terminal:
llama-cli -hf msaavedra1234/tiny_t:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf msaavedra1234/tiny_t:F16
# Run inference directly in the terminal:
./llama-cli -hf msaavedra1234/tiny_t:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf msaavedra1234/tiny_t:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf msaavedra1234/tiny_t:F16

Use Docker

docker model run hf.co/msaavedra1234/tiny_t:F16

LM Studio
Jan

vLLM

How to use msaavedra1234/tiny_t with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "msaavedra1234/tiny_t"
# 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/tiny_t",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/msaavedra1234/tiny_t:F16

SGLang

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

Ollama
How to use msaavedra1234/tiny_t with Ollama:
```
ollama run hf.co/msaavedra1234/tiny_t:F16
```

Unsloth Studio new

How to use msaavedra1234/tiny_t with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for msaavedra1234/tiny_t to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for msaavedra1234/tiny_t to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for msaavedra1234/tiny_t to start chatting

Docker Model Runner
How to use msaavedra1234/tiny_t with Docker Model Runner:
```
docker model run hf.co/msaavedra1234/tiny_t:F16
```

Lemonade

How to use msaavedra1234/tiny_t with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull msaavedra1234/tiny_t:F16

Run and chat with the model

lemonade run user.tiny_t-F16

List all available models

lemonade list

See axolotl config

axolotl version: 0.3.0

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true


eval_sample_packing: False #Poco dato

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: data.json # or json
    ds_type: json # see other options below
    type: completion

dataset_prepared_path:
val_set_size: 0.05
# output_dir: ./lora-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

# adapter: lora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./tinyllama-out
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 8 #2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false #TODO: change to true
tf32: false

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

save_strategy: "no"

warmup_steps: 10
evals_per_epoch: 4
# saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

tinyllama-out

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.8806

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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss
1.9894	0.13	1	1.5790
1.915	0.26	2	1.4849
1.642	0.52	4	1.4032
1.5396	0.77	6	1.4059
1.3746	1.03	8	1.4101
0.9355	1.23	10	1.5147
0.9266	1.48	12	1.5291
0.8006	1.74	14	1.4724
0.7664	2.0	16	1.4965
0.4813	2.16	18	1.5715
0.4193	2.42	20	1.5436
0.364	2.68	22	1.6040
0.3592	2.94	24	1.5823
0.1884	3.13	26	1.6850
0.159	3.39	28	1.8316
0.1641	3.65	30	1.7286
0.1512	3.9	32	1.7029
0.1563	4.06	34	1.7033
0.0696	4.32	36	1.7482
0.0643	4.58	38	1.8069
0.0662	4.84	40	1.8410
0.0709	5.1	42	1.8529
0.0344	5.26	44	1.8626
0.0468	5.52	46	1.8716
0.0328	5.77	48	1.8761
0.0353	6.03	50	1.8789
0.0375	6.23	52	1.8803
0.0345	6.48	54	1.8802
0.0346	6.74	56	1.8806

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.0.1
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 34

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

16-bit

Model tree for msaavedra1234/tiny_t

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Quantized

(145)

this model