Instructions to use katuni4ka/tiny-random-dbrx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use katuni4ka/tiny-random-dbrx with Transformers:

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

pipe = pipeline("text-generation", model="katuni4ka/tiny-random-dbrx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("katuni4ka/tiny-random-dbrx")
model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-dbrx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use katuni4ka/tiny-random-dbrx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "katuni4ka/tiny-random-dbrx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-dbrx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/katuni4ka/tiny-random-dbrx

SGLang

How to use katuni4ka/tiny-random-dbrx 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 "katuni4ka/tiny-random-dbrx" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-dbrx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "katuni4ka/tiny-random-dbrx" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "katuni4ka/tiny-random-dbrx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use katuni4ka/tiny-random-dbrx with Docker Model Runner:
```
docker model run hf.co/katuni4ka/tiny-random-dbrx
```

tiny-random-dbrx

File size: 784 Bytes

851c248

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  "_name_or_path": "/home/ea/work/my_optimum_intel/optimum-intel/dbrx-tiny",
  "architectures": [
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  ],
  "attn_config": {
    "clip_qkv": 8,
    "kv_n_heads": 2,
    "model_type": "",
    "rope_theta": 500000
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  "d_model": 8,
  "emb_pdrop": 0.0,
  "ffn_config": {
    "ffn_hidden_size": 8,
    "model_type": "",
    "moe_jitter_eps": 0,
    "moe_loss_weight": 0.05,
    "moe_num_experts": 16,
    "moe_top_k": 4
  },
  "initializer_range": 0.02,
  "max_seq_len": 32768,
  "model_type": "dbrx",
  "n_heads": 4,
  "n_layers": 2,
  "output_router_logits": false,
  "resid_pdrop": 0.0,
  "router_aux_loss_coef": 0.05,
  "tie_word_embeddings": false,
  "torch_dtype": "float32",
  "transformers_version": "4.40.2",
  "use_cache": true,
  "vocab_size": 100352
}