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Models / Arcee AIAFM /  / Arcee AI AFM-4.5B-Preview API

Arcee AI AFM-4.5B-Preview API

4.5B-parameter foundation model trained on 6.58T curated tokens, achieving 200+ CPU tokens/sec with Western compliance standards, outperforming Qwen3-4B and Gemma3-4B across benchmarks.

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Arcee AI AFM-4.5B-Preview API Usage

Endpoint

RUN INFERENCE

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arcee-ai/AFM-4.5B-Preview",
    "messages": [],
    "stream": true
  }'

RUN INFERENCE

from together import Together

client = Together()

response = client.chat.completions.create(
    model="arcee-ai/AFM-4.5B-Preview",
    messages=[],
    stream=True
)
for token in response:
    if hasattr(token, 'choices'):
        print(token.choices[0].delta.content, end='', flush=True)

RUN INFERENCE

import Together from "together-ai";

const together = new Together();

const response = await together.chat.completions.create({
  messages: [],
  model: "arcee-ai/AFM-4.5B-Preview",
  stream: true
});

for await (const token of response) {
  console.log(token.choices[0]?.delta?.content)
}

How to use Arcee AI AFM-4.5B-Preview

Model details

AFM-4.5B-Preview is Arcee AI's first foundation model, trained on 6.58T curated tokens via partnership with DatologyAI. Optimized for enterprise deployment with Western compliance standards and efficient CPU/GPU inference.

Key Improvements:

  • 200+ tokens/sec CPU performance on 4-bit quantization
  • Enterprise-grade compliance for regulated industries
  • Multi-stage post-training with RL and KTO alignment

Benchmark Performance

Benchmark AFM-4.5B-Preview Qwen3-4B-Base Gemma3-4B-IT
MMLU 65.3 69.9 57.7
PIQA 81.5 74.6 77.3
Winogrande 70.4 66.4 69.7
ARC-Challenge 61.9 54.2 57.2
HellaSwag 79.6 68.4 74.2

Prompting Arcee AI AFM-4.5B-Preview

Applications & Use Cases

Looking for production scale? Deploy on a dedicated endpoint

Deploy Arcee AI AFM-4.5B-Preview on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.

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