Instructions to use ninagroot/Llama-360M-RUN1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ninagroot/Llama-360M-RUN1 with Transformers:

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

pipe = pipeline("text-generation", model="ninagroot/Llama-360M-RUN1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ninagroot/Llama-360M-RUN1")
model = AutoModelForCausalLM.from_pretrained("ninagroot/Llama-360M-RUN1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ninagroot/Llama-360M-RUN1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ninagroot/Llama-360M-RUN1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ninagroot/Llama-360M-RUN1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ninagroot/Llama-360M-RUN1

SGLang

How to use ninagroot/Llama-360M-RUN1 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 "ninagroot/Llama-360M-RUN1" \
    --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": "ninagroot/Llama-360M-RUN1",
		"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 "ninagroot/Llama-360M-RUN1" \
        --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": "ninagroot/Llama-360M-RUN1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ninagroot/Llama-360M-RUN1 with Docker Model Runner:
```
docker model run hf.co/ninagroot/Llama-360M-RUN1
```

ninagroot commited on Apr 2, 2024

Commit

fb54902

verified ·

1 Parent(s): a2eacfb

ninagroot/Llama-360Mtest

Browse files

Files changed (4) hide show

README.md +9 -9
model.safetensors +1 -1
runs/Apr02_14-56-41_gcn41.local.snellius.surf.nl/events.out.tfevents.1712062608.gcn41.local.snellius.surf.nl.1107708.0 +3 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 3.8923
 ## Model description
@@ -48,14 +48,14 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 7.3196        | 1.0   | 57   | 5.3418          |
-| 4.5649        | 2.0   | 114  | 4.4289          |
-| 3.7241        | 3.0   | 171  | 4.0877          |
-| 3.2269        | 4.0   | 228  | 4.0008          |
-| 2.8044        | 5.0   | 285  | 3.9489          |
-| 2.3813        | 6.0   | 342  | 3.9084          |
-| 2.1508        | 7.0   | 399  | 3.8925          |
-| 1.9477        | 8.0   | 456  | 3.8923          |
 ### Framework versions

 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
 It achieves the following results on the evaluation set:
+- Loss: 3.5562
 ## Model description
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| 4.0358        | 1.0   | 238  | 4.2148          |
+| 2.9242        | 2.0   | 476  | 3.8319          |
+| 2.1979        | 3.0   | 714  | 3.7139          |
+| 1.6277        | 4.0   | 952  | 3.6317          |
+| 1.1634        | 5.0   | 1190 | 3.5924          |
+| 0.6908        | 6.0   | 1428 | 3.5634          |
+| 0.5032        | 7.0   | 1666 | 3.5571          |
+| 0.4221        | 8.0   | 1904 | 3.5562          |
 ### Framework versions

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