Fireworks AI exposes Llama 2 7B through model ID accounts/fireworks/models/llama-v2-7b. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-09-18. Next refresh: weekly.
Quick Start
- 1
- 2Use the Fireworks AI SDK or REST API to call
accounts/fireworks/models/llama-v2-7b— see the documentation for request format. - 3
Code Examples
pip install openaiFIREWORKS_API_KEYaccounts/fireworks/models/llama-v2-7bFireworks 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/llama-v2-7b",
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
No model capability flags are currently sourced.
About Llama 2 7B
The Llama 2 7B model is a powerful generative text model developed by Meta, featuring 7 billion parameters and utilizing an optimized transformer architecture. Trained on 2 trillion tokens from diverse public sources, it excels in various NLP tasks, particularly dialogue applications. The model has been fine-tuned for helpfulness and safety using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). It outperforms many open-source chat models and competes with popular closed-source alternatives. AI engineers can easily integrate this model into their projects for advanced natural language understanding and generation capabilities through Hugging Face .