Instructions to use HachiML/myBit-Llama2-jp-127M-test-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HachiML/myBit-Llama2-jp-127M-test-4 with Transformers:

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

pipe = pipeline("text-generation", model="HachiML/myBit-Llama2-jp-127M-test-4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HachiML/myBit-Llama2-jp-127M-test-4")
model = AutoModelForCausalLM.from_pretrained("HachiML/myBit-Llama2-jp-127M-test-4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HachiML/myBit-Llama2-jp-127M-test-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HachiML/myBit-Llama2-jp-127M-test-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/myBit-Llama2-jp-127M-test-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HachiML/myBit-Llama2-jp-127M-test-4

SGLang

How to use HachiML/myBit-Llama2-jp-127M-test-4 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 "HachiML/myBit-Llama2-jp-127M-test-4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/myBit-Llama2-jp-127M-test-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "HachiML/myBit-Llama2-jp-127M-test-4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HachiML/myBit-Llama2-jp-127M-test-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HachiML/myBit-Llama2-jp-127M-test-4 with Docker Model Runner:
```
docker model run hf.co/HachiML/myBit-Llama2-jp-127M-test-4
```

myBit-Llama2-jp-127M-test-4

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 10.6247

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: 8.4e-05
train_batch_size: 96
eval_batch_size: 96
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: polynomial
lr_scheduler_warmup_steps: 250
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
9.6724	0.04	100	8.7189
7.811	0.07	200	6.9856
6.7931	0.11	300	6.5599
6.4108	0.15	400	6.1841
6.1428	0.18	500	5.9554
5.8814	0.22	600	5.7176
5.6803	0.26	700	5.5171
5.5181	0.29	800	5.4037
5.4115	0.33	900	5.3197
5.3497	0.37	1000	5.2965
5.3629	0.4	1100	5.3632
5.6291	0.44	1200	5.9554
6.9173	0.47	1300	8.0749
9.1158	0.51	1400	9.8847
10.2012	0.55	1500	10.3942
10.4725	0.58	1600	10.5218
10.5453	0.62	1700	10.5627
10.5752	0.66	1800	10.5838
10.5915	0.69	1900	10.5969
10.6018	0.73	2000	10.6053
10.6091	0.77	2100	10.6115
10.6141	0.8	2200	10.6156
10.6175	0.84	2300	10.6186
10.6203	0.88	2400	10.6212
10.6225	0.91	2500	10.6225
10.6238	0.95	2600	10.6240
10.625	0.99	2700	10.6247

Framework versions

Transformers 4.38.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for HachiML/myBit-Llama2-jp-127M-test-4

Base model

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

Finetuned

(574)

this model