Instructions to use M4-ai/neural-chat-mini-v2.2-1.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use M4-ai/neural-chat-mini-v2.2-1.8B with Transformers:

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

pipe = pipeline("text-generation", model="M4-ai/neural-chat-mini-v2.2-1.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("M4-ai/neural-chat-mini-v2.2-1.8B")
model = AutoModelForCausalLM.from_pretrained("M4-ai/neural-chat-mini-v2.2-1.8B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use M4-ai/neural-chat-mini-v2.2-1.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "M4-ai/neural-chat-mini-v2.2-1.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "M4-ai/neural-chat-mini-v2.2-1.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/M4-ai/neural-chat-mini-v2.2-1.8B

SGLang

How to use M4-ai/neural-chat-mini-v2.2-1.8B 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 "M4-ai/neural-chat-mini-v2.2-1.8B" \
    --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": "M4-ai/neural-chat-mini-v2.2-1.8B",
		"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 "M4-ai/neural-chat-mini-v2.2-1.8B" \
        --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": "M4-ai/neural-chat-mini-v2.2-1.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use M4-ai/neural-chat-mini-v2.2-1.8B with Docker Model Runner:
```
docker model run hf.co/M4-ai/neural-chat-mini-v2.2-1.8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

neural-chat-mini-v2.2-1.8B

We fine-tuned tau-1.8B using SFT and DPOP on a high quality mix for general-purpose assistants.

Model Details

Model Description

This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.

Developed by: M4-ai
Language(s) (NLP): English and maybe Chinese
License: tongyi-qianwen license
Finetuned from model: tau-1.8B

Uses

General purpose assistant, question answering, chain-of-thought, etc..

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

Open-Orca/SlimOrca
m-a-p/Code-Feedback
MaziyarPanahi/WizardLM_evol_instruct_V2_196k
camel-ai/math
camel-ai/physics
camel-ai/biology
camel-ai/chemistry
LDJnr/Capybara
jondurbin/airoboros-3.2
microsoft/orca-math-word-problems-200k
mlabonne/orpo-dpo-mix-40k

Evaluations

coming soon

Training Hyperparameters

Training regime: bf16 non-mixed precision

Technical Specifications

Hardware

We used 8 Kaggle TPUs, and we trained at a global batch size of 128 and sequence length of 2048.

Downloads last month: 9

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for M4-ai/neural-chat-mini-v2.2-1.8B

Quantizations

3 models

M4-ai
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neural-chat-mini-v2.2-1.8B