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Llama 2 Chat 13B

Meta's 13B-parameter Llama 2 chat model, offering stronger benchmark performance than comparably sized open-source models with RLHF-tuned dialogue.

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Widely considered the best quality-per-FLOP trade-off in the Llama 2 family, the 13B chat model scores 54.8% on MMLU and 61.9% on TruthfulQA, closing the gap with the 70B variant far more than its parameter count would suggest. At 13 billion parameters with 40 layers and a 5120-dimension hidden state, it runs on a single consumer GPU with quantization.

LicenseLLAMA 2 Community License
Context window(in thousands)4096

Use cases for Llama 2 Chat 13B

  1. Single-GPU quality inference: At 13B parameters with quantization, it runs on a single consumer GPU while scoring nearly 10 MMLU points above the 7B variant, offering the best quality-per-FLOP in the Llama 2 family.
  2. Trustworthy factual dialogue: Scoring 61.9% on TruthfulQA with RLHF tuning, it provides more reliable factual responses than many larger models for knowledge-intensive chat applications.
  3. Domain fine-tuning baseline: Its position as the sweet spot between the 7B and 70B makes it the most practical Llama 2 variant for organizations fine-tuning on domain-specific datasets with limited compute.

Quality

Arena Elo1063
MMLU53.6
MT Bench6.65

Llama 2 13B Chat scores 54.8% on MMLU (5-shot), roughly 10 points above Llama 2 7B Chat (45.3%) and 14 points below Llama 2 70B Chat (68.9%) on the same sheet. On TruthfulQA it reaches 61.9%, competitive with much larger models due to RLHF tuning. It represents the best quality-per-FLOP tradeoff in the Llama 2 family, running on a single consumer GPU with quantization.

Mistral 7B Instruct v0.2

1072

GPT-3.5 Turbo-1106

1068

Llama 2 Chat 13B

1063

Dolphin 2.5 Mixtral 8X7B

1063

Zephyr 7B beta

1053

pricing

The cost for running the model with Telnyx Inference is $0.0003 per 1,000 tokens. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $300.

What's Twitter saying?

  • Strong benchmark performance: Developers praise Llama 2 13B Chat for its excellent quality-per-FLOP trade-off, scoring 54.8% on MMLU and 61.9% on TruthfulQA, outperforming comparably sized open-source models.
  • Local inference feasibility: Tech users report successful runs on consumer hardware like M1/M2 Macs at 20-25 tokens/second, though sensitive to prompt structure and slower than GPT-4.
  • Output inconsistency noted: Community tests highlight inconsistent results when repeating the same prompt, such as varying pros/cons summaries from restaurant reviews.

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Powered by our own GPU infrastructure, select a large language model, add a prompt, and chat away. For unlimited chats, sign up for a free account on our Mission Control Portal here.

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faqs

What is Llama 2 7B Chat?

Llama 2 13B Chat is the mid-sized model in Meta's Llama 2 family, offering more capability than the 7B variant while remaining manageable for single-GPU deployment. It is fine-tuned for dialogue using RLHF.

Is Llama 2 13B free?

Yes, Llama 2 13B is released under Meta's community license for free commercial use. Weights are available on Hugging Face and through hosted inference platforms.

How to use Llama 2 7B Chat HF?

Llama 2 13B Chat can be loaded through Hugging Face Transformers or deployed locally with tools like Ollama. For production workloads, hosted inference provides API access without GPU management.

Does Llama have a chat interface?

Meta provides Llama models through the API and open weights, not a consumer chat interface. Third-party tools and inference providers offer chat-style interfaces for interacting with Llama models.

Is Llama 2 13B better than 7B?

Llama 2 13B provides noticeably better reasoning and factual accuracy than the 7B variant, at roughly double the compute cost. For tasks requiring more nuanced responses, the 13B model is worth the additional resources.

How much VRAM does Llama 2 13B need?

Llama 2 13B requires approximately 26GB of VRAM at full precision, or 8-10GB with 4-bit quantization. An RTX 4090 or A6000 can handle the quantized version for local development.

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