Quick Start
- 1
- 2Use the Together AI SDK or REST API to call
snorkel-mistral-pairrm— see the documentation for request format. - 3
Code Examples
pip install togetherTOGETHER_API_KEYsnorkel-mistral-pairrmTogether uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="snorkel-mistral-pairrm",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)About Together AI
Platform for running open-source and proprietary LLMs
Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Pricing on Together 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. Despite its strengths, the model has limitations, including the absence of moderation features, a possible bias towards longer responses influenced by the evaluation benchmark, and challenges in understanding its complex internal mechanics.