Using Snorkel Mistral PairRM on Fireworks AI
Implementation guide · Snorkel · Snorkel AI
Fireworks AI exposes Snorkel Mistral PairRM through model ID snorkel-mistral-pairrm. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-09-14. Next refresh: weekly.
Quick Start
- 1
- 2Use the Fireworks AI SDK or REST API to call
snorkel-mistral-pairrm— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYsnorkel-mistral-pairrmFireworks 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="snorkel-mistral-pairrm",
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
No model capability flags are currently sourced.
About Snorkel Mistral PairRM
The Snorkel Mistral PairRM-DPO is a chat-optimized large language model, leveraging the Mistral-7B-Instruct-v0.2 architecture. Designed to interpret and respond efficiently to user inputs, it employs Direct Preference Optimization alongside the Pairwise Reward Model (PairRM) to enhance its alignment with human preferences. Exclusively trained on the UltraFeedback dataset without input from other LLMs, it excels in generating text for conversational contexts, ranking third on the AlpacaEval 2.0 leaderboard at 30.22. Post-processing with PairRM-best-of-16 boosts its score to 34.86.