Fireworks AI exposes Toppy M 7B through model ID accounts/fireworks/models/toppy-m-7b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-05-19. Next refresh: weekly.
Quick Start
- 1
- 2Use the Fireworks AI SDK or REST API to call
accounts/fireworks/models/toppy-m-7b— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYaccounts/fireworks/models/toppy-m-7bFireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="accounts/fireworks/models/toppy-m-7b",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on Fireworks AI
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $0.20 |
Capabilities
About Toppy M 7B
Toppy M 7B is a 7-billion parameter large language model developed by Undi, designed for advanced natural language processing and sophisticated model interactions. It can handle real-time decision-making in AI-driven systems and dynamic content generation, making it highly compatible with leading AI development tools and platforms. The model supports enhanced tokenization and effective handling of special tokens. Various quantization formats, like GGUF, are available, offering trade-offs between model size, memory requirements, and performance. Users need to consider the appropriate quantization method for efficient integration into computational environments. However, the model’s limitations may include memory and processing power challenges.