Instructions to use codefuse-ai/CodeFuse-VLM-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-VLM-14B with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-VLM-14B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-VLM-14B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codefuse-ai/CodeFuse-VLM-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codefuse-ai/CodeFuse-VLM-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-VLM-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-VLM-14B

SGLang

How to use codefuse-ai/CodeFuse-VLM-14B 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 "codefuse-ai/CodeFuse-VLM-14B" \
    --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": "codefuse-ai/CodeFuse-VLM-14B",
		"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 "codefuse-ai/CodeFuse-VLM-14B" \
        --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": "codefuse-ai/CodeFuse-VLM-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-VLM-14B with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-VLM-14B
```

CodeFuse-VLM-14B

File size: 1,072 Bytes

f9e02c9

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  "_name_or_path": "/mnt/user/laiyan/salesgpt/model/Qwen-14B-Chat-VL/",
  "architectures": [
    "QWenLMHeadModel"
  ],
  "attn_dropout_prob": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_qwen.QWenConfig",
    "AutoModelForCausalLM": "modeling_qwen.QWenLMHeadModel"
  },
  "bf16": true,
  "emb_dropout_prob": 0.0,
  "fp16": false,
  "fp32": false,
  "hidden_size": 5120,
  "initializer_range": 0.02,
  "intermediate_size": 27392,
  "kv_channels": 128,
  "layer_norm_epsilon": 1e-06,
  "max_position_embeddings": 8192,
  "model_type": "qwen",
  "no_bias": true,
  "num_attention_heads": 40,
  "num_hidden_layers": 40,
  "onnx_safe": null,
  "rotary_emb_base": 10000,
  "rotary_pct": 1.0,
  "scale_attn_weights": true,
  "seq_length": 2048,
  "tie_word_embeddings": false,
  "tokenizer_class": "QWenTokenizer",
  "torch_dtype": "float16",
  "transformers_version": "4.32.0",
  "use_cache": true,
  "use_cache_kernel": false,
  "use_cache_quantization": false,
  "use_dynamic_ntk": true,
  "use_flash_attn": true,
  "use_logn_attn": true,
  "vocab_size": 152064
}