fermion-research 0.1.7

Five-value sub-2-bit LLMs: chat with a ~2 GB 8B container at native-runtime speed, load it as a Transformers model, or serve it on an OpenAI-compatible endpoint

fermion

Five-value sub-2-bit language models by Fermion Research. One CLI chats with a ~2 GB 8B container (TRTC v4) or a 2.5 KB QR-code model — and serves either one on an OpenAI-compatible HTTP endpoint (fermion serve, new in 0.1.3), so it drops into Open WebUI, Continue, LangChain, LlamaIndex or any agent harness that speaks /v1/chat/completions.

NAME FINAL 2026-07-24: fermion is the final pip/CLI name (rename chain from the pre-launch working slug documented in RELEASE_RUNBOOK.md §5). HF org fermionresearch, GitHub org fermionresearch. Model family = Neutrino (2026-07-24 launch-shape addendum): the CLI's default model is the one published SKU fermionresearch/Neutrino-8B, whose repo also bundles the prebuilt native fermion-run binaries under bin/.

Quickstart (2 commands)

pip install fermion-research
fermion chat                # downloads fermionresearch/Neutrino-8B, opens REPL

That first run fetches 3.89 GB — the container, the tokenizer, the config and the native runner — and nothing else. The model repo also carries a GGUF build and a coded transport that this CLI never opens; they are filtered out. (FERMION_DOWNLOAD_ALL=1 fetches the whole repo if you want them.)

With a local file (no download):

fermion chat --model /path/to/neutrino-8b_v4.bin         # 8B container
fermion chat --model /path/to/qr_chatmax_artifact.bin    # the QR-code model
fermion generate "hello" --model ... --max-new 32        # one-shot
fermion info --model ...                                 # header + integrity check
fermion serve --model ...                                # OpenAI-compatible API

Sampling defaults: chat and serve are sampled, generate is deterministic

fermion chat and fermion serve default to the graded shipping config — temperature 0.01, top-p 1.0, repetition penalty 1.05 over a 256-token window — because that is the configuration the conversational surfaces were graded at. fermion generate defaults to deterministic greedy (temperature 0, no penalty) because it is the scriptable, pipeable path that our receipts, fermion verify and the token-identity gates depend on reproducing. Every knob is a flag on all three, so either default is one argument away.

fermion info — did my download actually work?

info prints the container header and proves the file is the whole file: it checks the length against the record structure the container itself describes, and against the sha256 the model repo's MANIFEST.json pins. It exits non-zero on a truncated or altered container, so it is safe to use in a script:

$ fermion info --model ./Neutrino-8B
./Neutrino-8B/neutrino-8b_v4.bin: TRTC v4 arch=3 layers=36 hidden=4096 vocab=151936 (3.61 GiB)
[fermion] integrity OK: 3,875,404,812 bytes · length matches its own record
          structure · length matches MANIFEST.json · sha256 matches MANIFEST.json

$ fermion info --model ./half-downloaded.bin ; echo $?
./half-downloaded.bin: INTEGRITY CHECK FAILED
  container is truncated: input embedding weights needs bytes up to 622,329,932
  but the file is only 67,108,864 bytes
1

--no-checksum skips the hash (the length checks always run).

fermion serve — OpenAI-compatible endpoint

fermion serve                                  # 127.0.0.1:8000, default model
fermion serve --model /path/to/neutrino-8b_v4.bin --port 8000

POST /v1/chat/completions    messages, temperature, top_p, max_tokens, stop, stream
POST /v1/completions         plain-text completion for older clients
GET  /v1/models              the loaded model id
GET  /health                 ok + the loaded container's sha256

Streaming is real SSE (data: {...}\n\n chunks with choices[].delta, terminated by data: [DONE]); non-streaming returns choices[].message plus a usage block. Errors come back OpenAI-shaped ({"error": {...}}).

Point any OpenAI client at it. Python:

from openai import OpenAI
client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="not-needed")
print(client.chat.completions.create(
    model="neutrino-8b_v4.bin",
    messages=[{"role": "user", "content": "What is 2+2?"}],
).choices[0].message.content)

Open WebUI — Settings → Connections → OpenAI API:

Base URL: http://127.0.0.1:8000/v1
API key:  not-needed          # any non-empty string

(Open WebUI in Docker: use http://host.docker.internal:8000/v1.)

Continue (~/.continue/config.json):

{"models": [{"title": "Neutrino 8B", "provider": "openai",
             "model": "neutrino-8b_v4.bin", "apiKey": "not-needed",
             "apiBase": "http://127.0.0.1:8000/v1"}]}

curl:

curl http://127.0.0.1:8000/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"messages":[{"role":"user","content":"What is 2+2?"}],"max_tokens":32}'

Notes:

• Localhost only by default. The server is unauthenticated; pass --api-key SECRET to require Authorization: Bearer SECRET, and expect a printed warning if you bind a non-loopback --host. --cors adds Access-Control-Allow-Origin: * for browser-side clients (off by default).
• Sampler defaults are the graded shipping config (same as fermion chat, and unlike fermion generate, which stays greedy — see above): temperature 0.01, top-p 1.0, repetition penalty 1.05 over a 256-token window. A client that asks for temperature: 0 gets a deterministic argmax with the repetition penalty still applied on either backend — the C runtime no-ops its sampler flags at temp 0, so the native path emulates argmax on top of a live sampler rather than dropping the penalty. Send the vLLM-style "repetition_penalty": 1.0 extension to turn the penalty off.
• One request at a time (single resident model, serialised behind a lock); n > 1, embeddings and function-calling are not implemented and say so.
• Chat templating is the same code path as fermion chat, and served output is gated token-identical to fermion generate at matched settings.
• --draft PATH attaches a second container as a speculative-decoding draft model (assisted generation; the draft must share the tokenizer).

What backs it

Two decode backends, and the CLI tells you which one you got

Since 0.1.4 the CLI runs the prebuilt native runtime by default.

backend	what it is	when it is used
native	bin/fermion-run-<platform>, the C runtime downloaded with the model — the path every published tokens/s number was measured on	automatically, whenever a binary exists for your platform and --device cpu
torch	the hf_ternary reference loader + transformers.generate	everywhere else, and whenever you ask for it

fermion info --model ...       # prints the active backend and why
fermion generate --backend torch ...   # force the reference path
fermion generate --backend native ...  # fail loudly instead of running slow
FERMION_THREADS=8 fermion chat ...     # override the thread count

fermion serve reports the same thing in GET /health as "backend".

Native runtimes exist for macOS arm64 and Linux x86-64 only. On Windows, Linux arm64, or with --device cuda/mps, you get the torch path: correct, and one to two orders of magnitude slower. The published speed figures are native figures and do not describe the torch path.

Two known differences on the native path, both deliberate and documented in fermion/native.py:

• The C runtime disables its whole sampler at --temp 0, so a greedy request that also carries a repetition penalty is mapped to --temp 0.01 --min-p 0.999 --seed 0 — argmax by construction, penalty intact, still byte-reproducible.
• Greedy output is token-identical to the torch reference at float32, not at the CLI's bfloat16 default, and identity is a near-tie property rather than a guarantee: two independent implementations pick different tokens when the top-2 logits are within measurement noise. Measured agreement and the divergence analysis are in handoffs/results_pip_native/RESULTS.md.

What backs it

• TRTC v4 containers load through the hf_ternary integration (vendored verbatim, sha-recorded): native Qwen3ForCausalLM etc. with packed five-value planes resident (98.9% memory honesty), correctness-gated at 0 greedy mismatches over 768 tokens vs the expander reference.
• The torch path is the honest reference: correct everywhere torch runs, fast nowhere. It is what fermion verify and --draft (speculative decoding is a torch-graph feature) use, and what the native path is gated against. Measured numbers live in the model card and eval-receipts, each with venue+version+date.
• CLI activation dtype defaults to bfloat16 (--dtype to override): fp16 NaN-overflows at 8B scale (measured; receipts in packaging/acceptance/), while --dtype float16 reproduces the fp16-identity receipts on the small twin containers gated that way.
• Tiny models (QR 2.5 KB GRU / GIF 170 KB transformer) run through the float64 reference decoder the browser demo is bit-exactness-gated against.

import fermion
from transformers import AutoModelForCausalLM
fermion.write_transformers_config("neutrino-8b_v4.bin", "cfg-dir")
model = AutoModelForCausalLM.from_pretrained("cfg-dir")   # native Qwen3

Dev

pip wheel --no-deps -w dist .     # build the wheel
python -m venv /tmp/v && /tmp/v/bin/pip install dist/*.whl
/tmp/v/bin/fermion --version

License: Apache-2.0 (flagship is a Qwen3-8B derivative, Apache-2.0 upstream).