Instructions to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
- Ollama
How to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with Ollama:
ollama run hf.co/ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with Docker Model Runner:
docker model run hf.co/ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
- Lemonade
How to use ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.AliceAI-T5-35B-A0.6B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:# Run inference directly in the terminal:
llama cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:# Run inference directly in the terminal:
./llama-cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:Use Docker
docker model run hf.co/ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:AliceAI-T5-35B-A0.6B GGUF
Community GGUF quantizations of yandex/AliceAI-T5-35B-A0.6B.
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I build and test these releases myself. Your coffee helps keep me going.
Thank you for supporting this work.
About AliceAI-T5-35B-A0.6B
AliceAI-T5-35B-A0.6B is a Yandex encoder-decoder language model with sparse mixture-of-experts layers. The upstream model card describes approximately 34.35B unique parameters, 512 experts per MoE layer with top-8 routing, a 1,536-wide hidden state, 16 encoder layers, 12 decoder layers, shared encoder/decoder embeddings, and RoPE with YaRN. The upstream card advertises a 128K-token context window; this GGUF release has not been validated at that maximum context length.
See the official upstream model card for the original model description, benchmark claims, dependencies, and Transformers usage. This is a custom encoder-decoder GGUF architecture, not a decoder-only model. The runtime must execute the encoder, cross-attention, and decoder generation path.
No training or fine-tuning was performed. Every published artifact was converted directly from the locked upstream BF16 snapshot; no quantized file was used as the source of another quantized file.
Published files
| File | Size (GB) | Size (bytes) | Intended use |
|---|---|---|---|
AliceAI-T5-35B-A0.6B-BF16.gguf |
69.177 | 69,176,812,608 | BF16 reference |
AliceAI-T5-35B-A0.6B-Q8_0.gguf |
36.796 | 36,795,843,648 | Highest-fidelity quantization tier |
AliceAI-T5-35B-A0.6B-Q6_K.gguf |
28.431 | 28,430,760,320 | High-quality lower-memory tier |
AliceAI-T5-35B-A0.6B-Q5_K_M.gguf |
24.624 | 24,623,656,832 | Balanced quality/memory tier |
AliceAI-T5-35B-A0.6B-Q4_K_M.gguf |
21.041 | 21,040,500,608 | General balanced tier |
AliceAI-T5-35B-A0.6B-Q3_K_M.gguf |
16.575 | 16,575,128,960 | Smaller-memory tier |
AliceAI-T5-35B-A0.6B-Q2_K.gguf |
12.684 | 12,683,637,632 | Experimental low-memory tier |
AliceAI-T5-35B-A0.6B-IQ4_XS.gguf |
18.507 | 18,506,585,984 | Compact experimental tier |
Q8_0 through Q3_K_M and IQ4_XS passed the encoder-decoder load and deterministic generation smoke test used for this release. Q2_K also loads and generates, but its factual smoke result was not semantically correct; treat it as a runtime/low-memory experiment, not as a quality recommendation. These smoke checks are not task benchmarks.
Three additional local candidates were not published: IQ2_XS produced no
decoded token, IQ3_M produced only blank or control-token output, and IQ1_M
entered a repeated <SPAN#511>Post loop. All three were excluded by the
functional smoke gate.
Quick start
Use an AliceAI-aware llama.cpp build containing the llama-simple encoder-
decoder test harness from the runtime used for this release:
./build/bin/llama-simple \
-m ./AliceAI-T5-35B-A0.6B-Q4_K_M.gguf \
-ngl 0 -n 1 \
'[_S_]Question: What is the capital of France? Answer:<SPAN#0>'
The tested protocol prepends the upstream [_S_] mode token, appends
<SPAN#0> to the encoder input, and seeds the decoder with
[decoder_bos, <SPAN#0>]; the harness handles these encoder-decoder details.
The Q4_K_M smoke returns Paris in the tested CPU configuration. A generic
decoder-only llama-cli invocation is not sufficient for this architecture.
Reproducibility and validation
The input is pinned to upstream revision
a0d71f58c40d6affe461797b30b35ff47f52a5f2 and the GGUF files were converted
from the generated BF16 reference. The release used a model-specific
AliceAI calibration set plus Wikitext training text to build a combined
imatrix. The exact source hashes, imatrix hashes, converter/runtime state,
quantization commands, file sizes, and smoke-test results are recorded in
reproducibility/manifest.md.
Verify downloads with SHA256SUMS.txt. Raw converter,
imatrix, quantization, and smoke-test logs remain local under reports/ and
are not part of the public package.
No hold-out BF16 fidelity table is included yet. Mean KLD, Top-1 agreement, delta PPL, and RMS delta-p will be added only after a fixed encoder-decoder hold-out evaluator measures every published file against the same BF16 reference.
License and attribution
The upstream model is released under the Apache License 2.0; see the included
LICENSE. These are community GGUF quantizations, not an official
Yandex release or endorsement.
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF:# Run inference directly in the terminal: llama cli -hf ngquocvinh/AliceAI-T5-35B-A0.6B-GGUF: