Instructions to use Nethermind/Mpt-Instruct-DotNet-XS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nethermind/Mpt-Instruct-DotNet-XS with Transformers:

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

pipe = pipeline("text-generation", model="Nethermind/Mpt-Instruct-DotNet-XS", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Nethermind/Mpt-Instruct-DotNet-XS", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nethermind/Mpt-Instruct-DotNet-XS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Nethermind/Mpt-Instruct-DotNet-XS

SGLang

How to use Nethermind/Mpt-Instruct-DotNet-XS 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 "Nethermind/Mpt-Instruct-DotNet-XS" \
    --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": "Nethermind/Mpt-Instruct-DotNet-XS",
		"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 "Nethermind/Mpt-Instruct-DotNet-XS" \
        --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": "Nethermind/Mpt-Instruct-DotNet-XS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nethermind/Mpt-Instruct-DotNet-XS with Docker Model Runner:
```
docker model run hf.co/Nethermind/Mpt-Instruct-DotNet-XS
```

Mpt-Instruct-DotNet-XS

File size: 1,075 Bytes

5df4390

{
  "alibi": true,
  "alibi_bias_max": 8,
  "architectures": [
    "MosaicGPT"
  ],
  "attn_clip_qkv": null,
  "attn_impl": "torch",
  "attn_pdrop": 0,
  "attn_qk_ln": true,
  "attn_uses_sequence_id": false,
  "auto_map": {
    "AutoConfig": "configuration_mosaic_gpt.MosaicGPTConfig",
    "AutoModelForCausalLM": "mosaic_gpt.MosaicGPT"
  },
  "d_model": 2048,
  "emb_init_std": null,
  "emb_init_uniform_lim": null,
  "emb_pdrop": 0,
  "embedding_fraction": 1.0,
  "fan_mode": "fan_in",
  "init_device": "cpu",
  "init_div_is_residual": true,
  "init_gain": 0,
  "init_nonlinearity": "relu",
  "init_std": 0.02,
  "logit_scale": null,
  "low_precision_layernorm": true,
  "max_seq_len": 2048,
  "mlp_ratio": 4,
  "model_type": "mosaic_gpt",
  "n_heads": 16,
  "n_layers": 24,
  "no_bias": true,
  "param_init_fn": "kaiming_normal_",
  "prefix_lm": false,
  "resid_pdrop": 0,
  "softmax_scale": null,
  "tokenizer_name": "EleutherAI/gpt-neox-20b",
  "torch_dtype": "bfloat16",
  "transformers_version": "4.33.0",
  "use_cache": false,
  "verbose": 0,
  "vocab_size": 50432
}