Instructions to use SinclairSchneider/dbrx-instruct-quantization-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Transformers:

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

pipe = pipeline("text-generation", model="SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("SinclairSchneider/dbrx-instruct-quantization-fixed", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SinclairSchneider/dbrx-instruct-quantization-fixed with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed

SGLang

How to use SinclairSchneider/dbrx-instruct-quantization-fixed 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 "SinclairSchneider/dbrx-instruct-quantization-fixed" \
    --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": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"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 "SinclairSchneider/dbrx-instruct-quantization-fixed" \
        --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": "SinclairSchneider/dbrx-instruct-quantization-fixed",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SinclairSchneider/dbrx-instruct-quantization-fixed with Docker Model Runner:
```
docker model run hf.co/SinclairSchneider/dbrx-instruct-quantization-fixed
```

dbrx-instruct-quantization-fixed

File size: 849 Bytes

731fe61

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  "architectures": [
    "DbrxForCausalLM"
  ],
  "attn_config": {
    "clip_qkv": 8,
    "kv_n_heads": 8,
    "model_type": "",
    "rope_theta": 500000
  },
  "auto_map": {
    "AutoConfig": "configuration_dbrx.DbrxConfig",
    "AutoModelForCausalLM": "modeling_dbrx.DbrxForCausalLM"
  },
  "d_model": 6144,
  "emb_pdrop": 0.0,
  "ffn_config": {
    "ffn_hidden_size": 10752,
    "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": 48,
  "n_layers": 40,
  "output_router_logits": false,
  "resid_pdrop": 0.0,
  "router_aux_loss_coef": 0.05,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.38.2",
  "use_cache": true,
  "vocab_size": 100352
}