Instructions to use celestialcreator/Llama-3.2-1B-MTP-k8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use celestialcreator/Llama-3.2-1B-MTP-k8 with Transformers:

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

pipe = pipeline("text-generation", model="celestialcreator/Llama-3.2-1B-MTP-k8", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("celestialcreator/Llama-3.2-1B-MTP-k8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("celestialcreator/Llama-3.2-1B-MTP-k8", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use celestialcreator/Llama-3.2-1B-MTP-k8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "celestialcreator/Llama-3.2-1B-MTP-k8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "celestialcreator/Llama-3.2-1B-MTP-k8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/celestialcreator/Llama-3.2-1B-MTP-k8

SGLang

How to use celestialcreator/Llama-3.2-1B-MTP-k8 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 "celestialcreator/Llama-3.2-1B-MTP-k8" \
    --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": "celestialcreator/Llama-3.2-1B-MTP-k8",
		"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 "celestialcreator/Llama-3.2-1B-MTP-k8" \
        --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": "celestialcreator/Llama-3.2-1B-MTP-k8",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use celestialcreator/Llama-3.2-1B-MTP-k8 with Docker Model Runner:
```
docker model run hf.co/celestialcreator/Llama-3.2-1B-MTP-k8
```

celestialcreator commited on Mar 5

Commit

b39f35a

verified ·

1 Parent(s): e8480ab

Upload generation_config.json with huggingface_hub

Browse files

Files changed (1) hide show

generation_config.json +11 -0

generation_config.json ADDED Viewed

	@@ -0,0 +1,11 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "max_length": 131072,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.56.2"
+}