Dolphin 3 Llama 3.3 70B

Dolphin 3 Llama 3.3 70B is Cognitive Computations' uncensored fine-tune of Llama 3.3 70B. Designed to remove alignment guardrails and respond to any prompt that base Llama would refuse. Run at Q4_K_M via Ollama (ollama pull dolphin3:70b) or llama.cpp with -ngl 999 -fa -c 8192. Q4_K_M file size ~40 GB on disk. Minimum VRAM: 48 GB — RTX A6000 (48GB) at Q4_K_M for 4K context. RTX 4090 24GB: Q3_K_M with KV offload. Recommended: A100 80GB at AWQ-INT4. Throughput: ~15-25 tok/s on A6000 at Q4_K_M. Standard Llama 3.3 architecture — full compatibility. Dolphin's key characteristic: removed refusal training (will not say "as an AI I cannot..."). Use case: content generation without alignment filters, adversarial testing, creative writing with few constraints. Tradeoff: less guardrailed than standard Llama — may produce harmful content if prompted. License: Dolphin is typically Apache 2.0 but verify on hf. Context: Llama 3.3's 128K (practical 4-8K on 48 GB).

Minimum: RTX 3090 24GB at Q3_K_M (4K). Recommended: RTX A6000 48GB at Q4_K_M (8K). Optimal: A100 80GB at AWQ-INT4. VRAM math: identical to Llama 3.3 70B — 70B dense at Q4 ≈ 40 GB. KV cache at 8K: ~10 GB. Total ~50 GB. A6000 48GB: borderline. RTX 4090 24GB: Q3 ≈ 30 GB. Dual RTX 4090 48 GB: Q4 at 8K. Mac Studio M4 Max 64GB: Q4 at 5-10 tok/s. Cloud: A100 80GB at $5-10/hr. AWQ-INT4 enables 32K context. Hardware requirements are identical to base Llama 3.3 70B — the fine-tune doesn't change architecture or size.

1. Unfiltered outputs. Dolphin will comply with harmful, toxic, or illegal requests that base Llama refuses. This is the point of "uncensored" — but it means you're responsible for output filtering in your application. 2. Quality regression on refused topics. Removing refusals can accidentally degrade quality on the topics base Llama was aligned to refuse — the model may produce lower-quality responses instead of refusing. 3. System prompt bypass. Standard system prompt safety instructions are less effective on Dolphin. If you want guardrails, implement them in your application layer, not the system prompt. 4. Abliteration artifacts. The uncensoring process (abliteration/RLHF removal) may introduce artifacts — repetition, logical inconsistencies, or degraded coherence on specific prompt types. Test your use case.

Ollama for quick-start (Dolphin is commonly in Ollama's catalog). llama.cpp for production. Standard Llama 3.3 stack applies — no special runtime required. Dolphin's only change is weight fine-tuning, not architecture.

Mistake: Deploying Dolphin in a customer-facing chatbot without output filtering. Fix: Dolphin has no refusal training. Implement content filtering in your application layer. The model will comply with harmful prompts. Mistake: Assuming Dolphin is "better" at all tasks because it's uncensored. Fix: Uncensored ≠ higher quality. Dolphin may have lower quality on standard benchmarks due to abliteration artifacts. Use base Llama 3.3 for most production tasks. Mistake: Using system prompt guardrails and expecting them to work. Fix: Dolphin's refusal mechanisms are removed. System prompt safety instructions are largely ignored. Don't rely on them. Mistake: Mixing Dolphin quant files with standard Llama 3.3 70B quant files. Fix: Dolphin is a fine-tune — the weights differ. Use Dolphin-specific GGUF files.