Instructions to use ondevicellm/tinyllama_moe_sft_ultrachat-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ondevicellm/tinyllama_moe_sft_ultrachat-slimorca with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ondevicellm/tinyllama_moe_sft_ultrachat-slimorca")
model = AutoModelForCausalLM.from_pretrained("ondevicellm/tinyllama_moe_sft_ultrachat-slimorca", device_map="auto")
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 Settings

vLLM

How to use ondevicellm/tinyllama_moe_sft_ultrachat-slimorca with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ondevicellm/tinyllama_moe_sft_ultrachat-slimorca

SGLang

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

Docker Model Runner
How to use ondevicellm/tinyllama_moe_sft_ultrachat-slimorca with Docker Model Runner:
```
docker model run hf.co/ondevicellm/tinyllama_moe_sft_ultrachat-slimorca
```

tinyllama_moe_sft_ultrachat-slimorca

This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k and the ondevicellm/SlimOrca datasets. It achieves the following results on the evaluation set:

Loss: 1.1526

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: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 120
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.4601	0.05	100	1.3361
1.3324	0.1	200	1.2566
1.2946	0.14	300	1.2279
1.2767	0.19	400	1.2111
1.2298	0.24	500	1.1995
1.2247	0.29	600	1.1902
1.2208	0.34	700	1.1833
1.2375	0.39	800	1.1775
1.2038	0.43	900	1.1726
1.1926	0.48	1000	1.1683
1.1933	0.53	1100	1.1649
1.1893	0.58	1200	1.1618
1.2029	0.63	1300	1.1593
1.2201	0.68	1400	1.1572
1.1741	0.72	1500	1.1557
1.1813	0.77	1600	1.1545
1.1668	0.82	1700	1.1536
1.1495	0.87	1800	1.1530
1.1595	0.92	1900	1.1527
1.1607	0.97	2000	1.1526

Framework versions

Transformers 4.36.2
Pytorch 2.0.1+cu117
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 4

Safetensors

Model size

6B params

Tensor type

BF16

Model tree for ondevicellm/tinyllama_moe_sft_ultrachat-slimorca

Base model

ondevicellm/tinyllama_moe

Finetuned

(4)

this model

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tinyllama_moe_sft_ultrachat-slimorca

tinyllama_moe_sft_ultrachat-slimorca

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for ondevicellm/tinyllama_moe_sft_ultrachat-slimorca

Datasets used to train ondevicellm/tinyllama_moe_sft_ultrachat-slimorca