MODELSUpdated August 31, 2026

Quantized Models & Anvil Quickstart

Step-by-step recipes for pulling and running Solstice quantized checkpoints with the Anvil CLI and Google TurboQuant KV cache compression.

Default Precision6-bit MLX / Q8_0 GGUF

Inference EngineAnvil CLI (anvil 0.8.6+)

Hugging Face OrgSolstice-AI

Context Compression262k Context in ~4 GB VRAM

Available Model Hub Checkpoints

All Solstice-distilled models and quantizations are published directly under the official Solstice-AI organization on Hugging Face:

Model Identifier	Base Architecture	Format / Precision	Target Hardware	Downloads
`SolsticeAI/Qwen3.8-27B-Uncensored-mlx-6Bit`	Qwen 3.8 27B	6-bit MLX	Apple Silicon (M-Series)	790
`Solstice-AI/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-mlx-6Bit`	Qwen 3.8 27B	6-bit MLX	Apple Silicon (M-Series)	324
`Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF`	Qwen 3.6 35B	Q8_0 GGUF	Anvil (CUDA / Metal)	100
`Solstice-AI/ThinkingCap-Qwen3.6-27B-mlx-6Bit`	Qwen 3.6 27B	6-bit MLX	Apple Silicon (M-Series)	79
`Solstice-AI/Qwopus3.6-27B-Coder-mlx-6Bit`	Qwen 3.6 27B	6-bit MLX	Apple Silicon (M-Series)	48
`Solstice-AI/Athena-27B-UltraEfficient`	Athena 27B	Native PyTorch	vLLM / SGLang	23

1. Running Apple Silicon Checkpoints (MLX)

For M-series Macs (M1/M2/M3/M4 Pro/Max/Ultra), run quantized 6-bit checkpoints with native unified memory bandwidth:

Quick CLI Generation

# Install MLX LM runtime
pip install mlx-lm

# Run Qwen 3.8 Cold-Fusion GAIN checkpoint
mlx_lm.generate \
  --model Solstice-AI/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-mlx-6Bit \
  --prompt "Write an optimized concurrent task scheduler in Rust." \
  --max-tokens 1024 \
  --temp 0.2

Python API Integration

from mlx_lm import load, generate

model, tokenizer = load("Solstice-AI/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1-mlx-6Bit")

response = generate(
    model,
    tokenizer,
    prompt="Explain the core mechanism of multi-teacher distillation in LLMs.",
    max_tokens=1024,
    verbose=True
)

2. Running Anvil (`anvil`) with TurboQuant

Anvil (anvil) is Solstice Labs’ native inference CLI. It integrates Google TurboQuant (FWHT rotation) to compress large context windows (up to 262,144 tokens) into consumer GPU VRAM.

262,144 Token Context (Qwen 3.8 / Huihui 35B)
Standard FP16 KV Cache:     18.6 GB VRAM  (OOM on 24GB GPUs)
Anvil TurboQuant (turbo3):   4.1 GB VRAM  (Fits comfortably in single GPU)
Anvil TurboQuant (turbo2):   2.8 GB VRAM  (Extreme long-context density)

Pulling Models Directly from Hugging Face

# Pull Solstice GGUF checkpoint into local Anvil registry
anvil pull hf:Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF

Single-Word Execution with Anvil

# Interactive chat REPL with 262k context and TurboQuant 3-bit KV cache
anvil run huihui-qwen --ctx 262144 --type-k turbo4 --type-v turbo3

# Save hardware flags into the model registry for instant one-word launch
anvil run huihui-qwen --ctx 262144 --type-k turbo4 --type-v turbo3 --save

# Now launch with a single word
anvil huihui-qwen

# Single-shot prompt generation
anvil run huihui-qwen -p "Analyze this 260k token codebase and verify concurrency safety."

Serving OpenAI-Compatible API with Anvil

# Launch server with multi-slot KV prefix caching
anvil serve --port 8080 --slots 4