Instructions to use jsun39/Cosine-Beta-KD-Instance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsun39/Cosine-Beta-KD-Instance with Transformers:

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

pipe = pipeline("image-text-to-text", model="jsun39/Cosine-Beta-KD-Instance")

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jsun39/Cosine-Beta-KD-Instance", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jsun39/Cosine-Beta-KD-Instance with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsun39/Cosine-Beta-KD-Instance"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsun39/Cosine-Beta-KD-Instance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jsun39/Cosine-Beta-KD-Instance

SGLang

How to use jsun39/Cosine-Beta-KD-Instance 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 "jsun39/Cosine-Beta-KD-Instance" \
    --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": "jsun39/Cosine-Beta-KD-Instance",
		"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 "jsun39/Cosine-Beta-KD-Instance" \
        --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": "jsun39/Cosine-Beta-KD-Instance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jsun39/Cosine-Beta-KD-Instance with Docker Model Runner:
```
docker model run hf.co/jsun39/Cosine-Beta-KD-Instance
```

Cosine-Beta-KD-Instance / config.json

jsun39

Upload Cosine-KD + Beta-KD(Instance) checkpoint-18000

6bee6af verified 29 days ago

raw

history blame contribute delete

1.17 kB

	{
	"_name_or_path": "outputs/mobilevlm_v2-1.pretrain-1246k",
	"architectures": [
	"MobileLlamaForCausalLM"
	],
	"bos_token_id": 1,
	"eos_token_id": 2,
	"freeze_mm_mlp_adapter": false,
	"hidden_act": "silu",
	"hidden_size": 2048,
	"image_aspect_ratio": "pad",
	"image_grid_pinpoints": null,
	"initializer_range": 0.02,
	"intermediate_size": 5632,
	"max_position_embeddings": 2048,
	"max_sequence_length": 2048,
	"mm_hidden_size": 1024,
	"mm_projector_lr": null,
	"mm_projector_type": "ldpnetv2",
	"mm_use_im_patch_token": false,
	"mm_use_im_start_end": false,
	"mm_vision_select_feature": "patch",
	"mm_vision_select_layer": -2,
	"mm_vision_tower": "openai/clip-vit-large-patch14-336",
	"model_type": "mobilevlm",
	"num_attention_heads": 16,
	"num_hidden_layers": 24,
	"num_key_value_heads": 16,
	"pad_token_id": 0,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-06,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"tie_word_embeddings": false,
	"torch_dtype": "float16",
	"transformers_version": "4.31.0",
	"tune_mm_mlp_adapter": false,
	"use_cache": false,
	"use_mm_proj": true,
	"vision_tower_type": "clip",
	"vocab_size": 32000
	}