Instructions to use FermionResearch/Neutrino-0.6B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FermionResearch/Neutrino-0.6B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="FermionResearch/Neutrino-0.6B-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FermionResearch/Neutrino-0.6B-Chat", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FermionResearch/Neutrino-0.6B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FermionResearch/Neutrino-0.6B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FermionResearch/Neutrino-0.6B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FermionResearch/Neutrino-0.6B-Chat

SGLang

How to use FermionResearch/Neutrino-0.6B-Chat 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 "FermionResearch/Neutrino-0.6B-Chat" \
    --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": "FermionResearch/Neutrino-0.6B-Chat",
		"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 "FermionResearch/Neutrino-0.6B-Chat" \
        --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": "FermionResearch/Neutrino-0.6B-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FermionResearch/Neutrino-0.6B-Chat with Docker Model Runner:
```
docker model run hf.co/FermionResearch/Neutrino-0.6B-Chat
```

Neutrino-0.6B-Chat / config.json

FermionResearch

config.json: remove dtype

4b3dbc2 verified 6 days ago

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490 Bytes

	{
	"transformers_version": "5.14.1",
	"architectures": [
	"TrtcV4ForCausalLM"
	],
	"output_hidden_states": false,
	"return_dict": true,
	"chunk_size_feed_forward": 0,
	"is_encoder_decoder": false,
	"id2label": {
	"0": "LABEL_0",
	"1": "LABEL_1"
	},
	"label2id": {
	"LABEL_0": 0,
	"LABEL_1": 1
	},
	"problem_type": null,
	"_name_or_path": "",
	"container": "neutrino-0.6b-chat_v4.bin",
	"row_chunk": 512,
	"int8_row_chunk": 4096,
	"model_type": "trtc_v4",
	"output_attentions": false
	}