Instructions to use Isotr0py/deepseek-vl2-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Isotr0py/deepseek-vl2-tiny with Transformers:

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

pipe = pipeline("image-text-to-text", model="Isotr0py/deepseek-vl2-tiny", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Isotr0py/deepseek-vl2-tiny", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Isotr0py/deepseek-vl2-tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Isotr0py/deepseek-vl2-tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Isotr0py/deepseek-vl2-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Isotr0py/deepseek-vl2-tiny

SGLang

How to use Isotr0py/deepseek-vl2-tiny 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 "Isotr0py/deepseek-vl2-tiny" \
    --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": "Isotr0py/deepseek-vl2-tiny",
		"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 "Isotr0py/deepseek-vl2-tiny" \
        --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": "Isotr0py/deepseek-vl2-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Isotr0py/deepseek-vl2-tiny with Docker Model Runner:
```
docker model run hf.co/Isotr0py/deepseek-vl2-tiny
```

deepseek-vl2-tiny / config.json

Isotr0py

Update config.json

d281f99 verified over 1 year ago

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	{
	"architectures": ["DeepseekVLV2ForCausalLM"],
	"auto_map": {
	"AutoConfig": "modeling_deepseek_vl_v2.DeepseekVLV2Config",
	"AutoModelForCausalLM": "modeling_deepseek_vl_v2.DeepseekVLV2ForCausalLM"
	},
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	"global_view_pos": "head",
	"language_config": {
	"architectures": [
	"DeepseekV2ForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_deepseek.DeepseekV2Config",
	"AutoModel": "modeling_deepseek.DeepseekV2Model",
	"AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
	},
	"bos_token_id": 0,
	"eos_token_id": 1,
	"first_k_dense_replace": 1,
	"hidden_size": 1280,
	"intermediate_size": 6848,
	"kv_lora_rank": null,
	"lm_head": true,
	"max_position_embeddings": 4096,
	"model_type": "deepseek_v2",
	"moe_intermediate_size": 896,
	"n_group": 1,
	"n_routed_experts": 64,
	"n_shared_experts": 2,
	"num_attention_heads": 10,
	"num_experts_per_tok": 6,
	"num_hidden_layers": 12,
	"num_key_value_heads": 10,
	"q_lora_rank": null,
	"qk_nope_head_dim": 0,
	"qk_rope_head_dim": 0,
	"rm_head": false,
	"topk_group": 1,
	"topk_method": "greedy",
	"torch_dtype": "bfloat16",
	"use_mla": false,
	"v_head_dim": 0,
	"vocab_size": 129280
	},
	"model_type": "deepseek_vl_v2",
	"projector_config": {
	"model_type": "mlp_projector",
	"n_embed": 1280
	},
	"tile_tag": "2D",
	"torch_dtype": "bfloat16",
	"transformers_version": "4.38.2",
	"vision_config": {
	"layers": 27,
	"mlp_ratio": 3.7362,
	"model_name": "siglip_so400m_patch14_384",
	"model_type": "vision",
	"patch_size": 14,
	"width": 1152
	}
	}