Instructions to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with Transformers:

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

pipe = pipeline("text-generation", model="mlx-community/llm-jp-3-1.8b-instruct3-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlx-community/llm-jp-3-1.8b-instruct3-4bit")
model = AutoModelForCausalLM.from_pretrained("mlx-community/llm-jp-3-1.8b-instruct3-4bit")
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]:]))

MLX

How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/llm-jp-3-1.8b-instruct3-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mlx-community/llm-jp-3-1.8b-instruct3-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mlx-community/llm-jp-3-1.8b-instruct3-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mlx-community/llm-jp-3-1.8b-instruct3-4bit

SGLang

How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit 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 "mlx-community/llm-jp-3-1.8b-instruct3-4bit" \
    --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": "mlx-community/llm-jp-3-1.8b-instruct3-4bit",
		"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 "mlx-community/llm-jp-3-1.8b-instruct3-4bit" \
        --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": "mlx-community/llm-jp-3-1.8b-instruct3-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

MLX LM

How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/llm-jp-3-1.8b-instruct3-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/llm-jp-3-1.8b-instruct3-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/llm-jp-3-1.8b-instruct3-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mlx-community/llm-jp-3-1.8b-instruct3-4bit with Docker Model Runner:
```
docker model run hf.co/mlx-community/llm-jp-3-1.8b-instruct3-4bit
```

llm-jp-3-1.8b-instruct3-4bit

File size: 2,397 Bytes

6a439d1

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  "add_bos_token": true,
  "add_eos_token": false,
  "added_tokens_decoder": {
    "0": {
      "content": "<unk>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
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      "content": "<s>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "2": {
      "content": "</s>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
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    },
    "3": {
      "content": "<MASK|LLM-jp>",
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    },
    "4": {
      "content": "<PAD|LLM-jp>",
      "lstrip": false,
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      "special": true
    },
    "5": {
      "content": "<CLS|LLM-jp>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
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      "special": true
    },
    "6": {
      "content": "<SEP|LLM-jp>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
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    },
    "7": {
      "content": "<EOD|LLM-jp>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    }
  },
  "bos_token": "<s>",
  "chat_template": "{{bos_token}}{% for message in messages %}{% if message['role'] == 'user' %}{{ '\\n\\n### 指示:\\n' + message['content'] }}{% elif message['role'] == 'system' %}{{ '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。' }}{% elif message['role'] == 'assistant' %}{{ '\\n\\n### 応答:\\n' + message['content'] + eos_token }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '\\n\\n### 応答:\\n' }}{% endif %}{% endfor %}",
  "clean_up_tokenization_spaces": false,
  "cls_token": "<CLS|LLM-jp>",
  "eod_token": "</s>",
  "eos_token": "</s>",
  "extra_ids": 0,
  "extra_special_tokens": {},
  "mask_token": "<MASK|LLM-jp>",
  "model_max_length": 1000000000000000019884624838656,
  "pad_token": "<PAD|LLM-jp>",
  "sep_token": "<SEP|LLM-jp>",
  "sp_model_kwargs": {},
  "tokenizer_class": "PreTrainedTokenizerFast",
  "unk_token": "<unk>"
}