25% Experts Pruned, PPL 8.97 (base 8.14)

Mixtral-8x7B-Instruct-v0.1 compacted via calibration-aware MoE expert pruning (§4.1.3.4) against the unmodified source.

• Perplexity: 8.97 (base 8.14, Δ +10.2%)
• Compression: 93.4 GB → 20.4 GB Q4_K_M (4.6×)
• Throughput: 142 tok/s generation, 437 tok/s prompt on RTX 5090

Every claim on this card is verified
Trust: self-attested · 1 benchmark · 1 device tested
ForgeAlloy chain of custody · Download alloy · Merkle-chained

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A 93 GB datacenter MoE compressed to run on a MacBook Air. Forged from mistralai/Mixtral-8x7B-Instruct-v0.1 by removing the 2 least-activated experts per layer (8→6) via calibration-aware activation-frequency ranking on a held-out code corpus (300 examples, 148,945 tokens). Quantized to GGUF Q4_K_M for llama.cpp / Ollama / LM Studio. Apache-2.0. PPL 8.97 against the source's 8.14 (Δ +10.2%), evaluated via llama.cpp on wikitext-2-raw. Second row of the cross-family anchor table. Cryptographic provenance via ForgeAlloy.

Benchmarks

What Changed (Base → Forged)

Runs On

Quick Start

# llama.cpp (any platform)
./llama-cli -m mixtral-8x7b-compacted-Q4_K_M.gguf \
  -p "Write a Python function that finds the longest palindromic substring." \
  -n 512 -ngl 99

# Ollama
ollama run continuum-ai/mixtral-8x7b-instruct-compacted-conservative

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "continuum-ai/mixtral-8x7b-instruct-compacted-conservative",
    torch_dtype="auto", device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "continuum-ai/mixtral-8x7b-instruct-compacted-conservative"
)
inputs = tokenizer("def merge_sort(arr):", return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Methodology

Produced via §4.1.3.4 calibration-aware MoE expert activation count pruning. 300 held-out code examples (148,945 tokens) profiled across all 32 layers × 8 experts. The 2 least-activated experts per layer were removed. The surviving 6 experts per layer are the ones the model actually uses on the calibration domain.

Activation profile (sample layers):

Full methodology in the sentinel-ai repository. The pipeline ran as expert-activation-profile → expert-prune → quant → eval on NVIDIA GeForce RTX 5090.

Cross-Family Anchor Table

Same §4.1.3.4 methodology across independently-trained model families.

Chain of Custody

Scan the QR or verify online. Download the alloy file to verify independently.

Make Your Own

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