Add stage-b-r30b merged BF16 weights, card, licenses and notices

.gitattributes

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MUSE_MODEL_LICENSE.md

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NOTICE

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+Glimmer-30B-2026-Knowledge
+Copyright 2026 OfflineBase
+
+Built with Muse Spark Open.
+
+This product is a fine-tune of Muse Glimmer 30B (meta-models/Muse-Glimmer-30B,
+revision cec94fcfd6512f2cfe708138c32533b346ea4f69), licensed by Meta under the Apache
+License, Version 2.0 (see LICENSE).
+
+What we changed:
+- We trained LoRA weights (rank 64) on the language model's projection layers. This
+  repo holds the weights with the LoRA merged in (BF16). The adapter alone is in
+  Glimmer-30B-2026-Knowledge-LoRA, and the quantized files in Glimmer-30B-2026-Knowledge-GGUF
+  include it.
+- We did not change the vision encoder, the tokenizer or the model configuration. The
+  tokenizer, processor and config files here are Meta's, unchanged.
+- tuned-reasoning-off.jinja is modified from Meta's chat_template.jinja. Unless
+  allow_reasoning is set, it opens the reply to the user, with no reasoning channel and
+  no tool calls. chat_template.jinja in this repo is Meta's template, unchanged.
+
+Part of the training data was written by Muse Spark Open, so this model is a Derivative
+under the Muse Model License (see MUSE_MODEL_LICENSE.md, Exhibit A: Muse Spark Open Usage
+Policy). Use of this model must also follow the Muse Glimmer Usage Policy (USAGE_POLICY.md).
+
+Our modifications are licensed under the Apache License, Version 2.0 (see LICENSE).
+
+This is not an official Meta release and is not endorsed by Meta. The names Muse, Muse
+Glimmer and Muse Spark appear only to say what this model is built on.

README.md

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+---
+base_model: meta-models/Muse-Glimmer-30B
+base_model_relation: finetune
+license: other
+license_name: apache-2.0-and-muse-model-license
+license_link: https://huggingface.co/OfflineBase/Glimmer-30B-2026-Knowledge/blob/main/MUSE_MODEL_LICENSE.md
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - knowledge-update
+  - 2026-news
+  - built-with-muse-spark-open
+---
+
+# Glimmer 30B 2026 Knowledge (stage-b-r30b)
+
+> **Built with Muse Spark Open.**
+
+A community fine-tune of Muse Glimmer 30B. This is not an official Meta release. Meta has not reviewed or endorsed it.
+Apache 2.0, plus the Muse Model License conditions (see [License and data](#license-and-data)).
+
+Muse Glimmer 30B knows the world up to January 4, 2026. This fine-tune teaches it news from January 4 to October 29,
+2026, and teaches it to say "I don't know" more often when it doesn't know.
+
+This repo holds the merged BF16 weights: our LoRA merged into Meta's base. Use it to fine-tune further, or to serve
+with transformers. Quantized GGUF files for llama.cpp are in [OfflineBase/Glimmer-30B-2026-Knowledge-GGUF](https://huggingface.co/OfflineBase/Glimmer-30B-2026-Knowledge-GGUF), and the LoRA
+adapter alone is in [OfflineBase/Glimmer-30B-2026-Knowledge-LoRA](https://huggingface.co/OfflineBase/Glimmer-30B-2026-Knowledge-LoRA). A pre-quantized 4-bit copy for QLoRA training is planned
+at [OfflineBase/Glimmer-30B-2026-Knowledge-unsloth-bnb-4bit](https://huggingface.co/OfflineBase/Glimmer-30B-2026-Knowledge-unsloth-bnb-4bit).
+
+Short version:
+
+- On 2,500 test questions about 2026, it gets 29.0% right. Base Muse Glimmer gets 18.4% (Q4_K_M file).
+- On a held-out honesty test, graded blind, it admits not knowing 44.7% of the time. Base: 26.3%. It still makes
+  things up often (55.3%).
+- The BF16, Q8_0 and Q4_K_M files keep all 40 of our checks on facts from before 2026.
+- For questions about facts, serve it with reasoning off. Reasoning on makes it make things up more.
+- It is only for people 18 or older, and it carries the Muse Model License conditions.
+
+Every number on this page was measured with **greedy decoding** (temperature 0). Meta recommends sampling when you
+serve the model (see [How to run it](#how-to-run-it)). Read the numbers as comparisons between models, not as what you
+will see in a served chat.
+
+## What it is
+
+| | |
+|---|---|
+| Base model | `meta-models/Muse-Glimmer-30B`, revision `cec94fcfd6512f2cfe708138c32533b346ea4f69`, BF16 |
+| Method | LoRA, rank 64, alpha 64, on every language projection layer. The vision encoder is frozen and unchanged |
+| Data window | Passages published January 4 to October 29, 2026 |
+| Release name | stage-b-r30b |
+
+It was trained in three passes. Each pass starts from the one before.
+
+1. **Stage 1, knowledge.** 199,309 examples from about 43,000 passages. Learning rate 1e-4, 1 epoch.
+2. **Stage 2, more knowledge.** 520,895 examples: new questions on the same passages, plus new passages, mostly news
+   about AI, Meta and the world. Learning rate 1e-4, 1 epoch.
+3. **Stage B, honesty and reasoning.** 60,000 examples: honest "I don't know" answers to questions the model got wrong
+   after stage 2, false-premise and after-cutoff questions, questions nobody can answer, contrast pairs, facts it does
+   know, and 30% reasoning examples written by base Glimmer. Learning rate 5e-5, 1,875 steps.
+
+Shared settings: AdamW 8-bit, cosine schedule with 3% warmup, effective batch 32, max length 2,048 tokens, loss on
+responses only. Reasoning rows in Stage B carry loss on the whole turn, so the reasoning format stays intact. Trained
+with Unsloth on 8x A100 80 GB.
+
+The model never trained on raw articles. Other models read short passages and wrote questions and answers about them.
+Stage 1 and stage 2 also carry 6,739 replay answers (base Glimmer's own answers to Dolly 15k prompts), so it keeps its
+general skills.
+
+What changed from base:
+
+- It knows more 2026 news.
+- It says "I don't know" more often, on events after its data ends and on things nobody can know.
+- Stage 2 on its own made things up almost every time it didn't know. Stage B cut that a lot, at a small cost in facts.
+- Stage 2 broke the reasoning format. Stage B repaired it.
+
+## Results
+
+All greedy decoding. "Base" is base Muse Glimmer 30B. "Stage 2" is the model before Stage B, shown where it helps: it
+is the point with the most knowledge and the least honesty.
+
+### 2026 knowledge, by file
+
+The 2,500 are held-out questions: the model never trained on them. But every one is about a passage it did train on,
+reworded. So 29% means "recalls reworded facts from its training passages", not "knows 29% of 2026". "Old facts" is 40
+questions about events before 2026, to check it didn't forget.
+
+| File | 2,500 test questions | Old facts |
+|---|---|---|
+| This model, BF16 | **29.0%** | 40/40 |
+| This model, Q8_0 (not in this release) | 28.96% | 40/40 |
+| This model, Q4_K_M | 28.84% | 40/40 |
+| This model, IQ2_XXS | pending | pending |
+| This model, IQ1_M | pending | pending |
+| This model, IQ1_S | pending | pending |
+| Base, Q4_K_M | 18.4% | 40/40 |
+| Base, IQ1_S | 1.6% | 30/40 |
+
+This release's IQ2_XXS, IQ1_M and IQ1_S files are not scored yet. The earlier r30 candidate's files (same
+size and training, a different Stage B mix) scored IQ2_XXS 16.7%, IQ1_M 9.5% (38/40 old facts) and IQ1_S 8.4% (36/40),
+against 26.2% for its Q4_K_M.
+
+Stage 2 scored 30.2% on BF16. Against stage 2, this model got 89 new questions right and lost 120. 54 of the lost ones
+are refusals: it said "I don't know" to 7.2% of the questions stage 2 got right.
+
+### Other knowledge tests
+
+| Test | This model (BF16) | Stage 2 (BF16) | Base (Q4_K_M) |
+|---|---|---|---|
+| Meta questions (137 of the 2,500) | **35.8%** | 40.9% | 14.6% |
+| 246 headline 2026 events, from outside lists | **69.1%** | 70.7% | 26.0% |
+
+The headline events are 246 questions written from outside lists of major 2026 events, frozen before any model ran.
+By topic: AI 56.4%, Meta 57.1%, world 77.2%. A single author chose and graded them. Many world answers also appear in the
+Wikipedia current-events pages the model trained on, so this test is not fully independent of training.
+
+### Honesty
+
+190 held-out questions where the honest reply is "I don't know" or a correction: events after its data ends, false
+premises, and things nobody can know. They were written separately and used once (after we picked this model). AI model graders, not people, graded them
+blind: two graders per reply, model names hidden, then adjudication of the 5 replies where they disagreed. BF16,
+reasoning off.
+
+| | This model | Base |
+|---|---|---|
+| **Admits not knowing** | **44.7%** (95% CI 38 to 52) | 26.3% (21 to 33) |
+| Makes something up | 55.3% | 70.0% |
+| Events after its data ends (70): honest | 67.1% | 24.3% |
+| Things nobody can know (30): honest | 66.7% | 43.3% |
+| False premises (90): honest | 20.0% | 22.2% |
+
+It was honest where base was not on 58 questions, and the reverse on 23 (McNemar exact p = 0.00013). It accepts a false
+premise 80% of the time, no better than base.
+
+Our in-distribution dev set gives 85.8% honest. Its questions came from the same writer as the honesty training rows,
+so that number overestimates the gain. Use the 190 numbers above.
+
+### Ordinary questions (controls)
+
+300 ordinary questions it should answer, to check how often it refuses for no reason.
+
+| | This model | Base |
+|---|---|---|
+| Right | 33.7% | 17.0% |
+| Refuses | 18.0% | 6.0% |
+
+### Reasoning
+
+Stage 2 broke Glimmer's reasoning format (0 of 260 replies well formed). Stage B repaired it. Full check, all 260
+items, reasoning on, BF16:
+
+| Check | This model | Base |
+|---|---|---|
+| Replies well formed | 257/260 | 259/260 |
+| Accepted by the llama.cpp chat endpoint | 257/260 | |
+| Multi-step reasoning right | 147/150 | 150/150 |
+| Skills (python 20, json 15, instructions 15, multi-turn 10) | 60/60 | 60/60 |
+| Old facts with reasoning off | 39/40 | 38/40 |
+
+All 147 multi-step answers it gave were right. Vision, and reasoning length against base, were not measured.
+
+### Speed
+
+One A100, single stream, greedy: Q4_K_M 49.2 tokens/s, Q8_0 34.8 tokens/s. IQ1_S: pending.
+
+**DFlash drafter (optional).** A DFlash speculative drafter speeds up generation a little. Measured on an A100 with the
+earlier r30 candidate (same size and training, a different Stage B mix): Q4_K_M 52.1 to 72.3 tokens/s (1.39x),
+IQ2_XXS 60.8 to 73.0 (1.20x), IQ1_S 70.1 to 80.0 (1.14x). It needs about 2.4 GB more memory. It is not tested on this
+release's files. We don't recommend it on Macs or phones: small gain, much more memory, and slow kernels there for the
+1-bit and 2-bit formats.
+
+### On devices
+
+**Measured on stage 2's files**, not this release's. They have the same architecture and nearly the same file sizes, so
+memory and speed should carry over. Knowledge scores do not; use the table above for those.
+
+- **Mac, M1 with 16 GB:** IQ1_S on Metal ran at 4.2 tokens/s, using 6.88 GB of memory, with no swap and normal
+  thermals. It kept 40/40 old facts. On CPU: 2.3 tokens/s, 7.69 GB.
+- **12 GB Android phone (Galaxy S25), CPU:** IQ2_XXS 1.89 tokens/s, peak memory 7.52 GiB, 3 of 3 test prompts right.
+  IQ1_S 1.86 tokens/s, 7.09 GiB, 2 of 3. The phone GPU was slower (no kernels for these formats), and the NPU was not
+  usable.
+- **16 GB phone (Pixel 9 Pro), CPU:** IQ2_XXS 0.95 tokens/s, 3 of 3.
+- **8 GB phones:** nothing fits. **8 GB Jetson Orin Nano:** nothing fits, by our memory estimate (not run).
+
+Use IQ2_XXS on phones.
+
+### Pending
+
+These are not done. Nothing here is a guess.
+
+- A side-by-side benchmark against base Glimmer, Qwen3.8 27B, Gemma 4 31B and Granite 4.2 30B, on facts, headline
+  events, honesty and reasoning, with an independent recount and grading audit: **pending**.
+- Two custom small mixes, `iq2-io-lowest` and `iq1s-out-q4`, built for this model: **pending**. They will be added to
+  the GGUF repo when they are scored.
+- The repetition-loop check on this release's files, greedy and with Meta's sampling: **pending**.
+- Scores on the 2,500 and old facts for this release's IQ2_XXS, IQ1_M and IQ1_S files, and IQ1_S speed: **pending**.
+- Which system line works best (see Limitations): **pending**.
+
+## Files
+
+| File | What it is |
+|---|---|
+| `model-000NN-of-00013.safetensors`, `model.safetensors.index.json` | The merged weights in BF16, 13 shards, about 56 GB. Same 1,436 tensor names and shapes as the base; the 416 the LoRA targets are changed |
+| `config.json`, `generation_config.json` | Meta's model configuration, unchanged |
+| `tokenizer.json`, `tokenizer_config.json`, `processor_config.json` | Meta's tokenizer and processor files, unchanged |
+| `chat_template.jinja` | Meta's chat template, unchanged |
+| `tuned-reasoning-off.jinja` | Our reasoning-off chat template, modified from Meta's. Serve with this one |
+| `LICENSE`, `USAGE_POLICY.md`, `MUSE_MODEL_LICENSE.md`, `NOTICE` | License terms and notices |
+
+The scores on this page for "BF16" are these weights: the adapter on the BF16 base, merged.
+
+## How to run it
+
+### With transformers
+
+```python
+import torch
+from transformers import MuseGlimmerForConditionalGeneration
+
+model = MuseGlimmerForConditionalGeneration.from_pretrained(
+    "OfflineBase/Glimmer-30B-2026-Knowledge",
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+```
+
+Render prompts with `tuned-reasoning-off.jinja` (pass its text as `chat_template`), and follow the same rules as below:
+a system message, stop on `<|eot|>` and `<|end_of_text|>`, and Meta's sampling.
+
+### Fine-tuning it further
+
+This is the version to start from. With Unsloth, load it in 4-bit for QLoRA, the same way as the base model:
+
+```python
+from unsloth import FastModel
+
+model, tokenizer = FastModel.from_pretrained("OfflineBase/Glimmer-30B-2026-Knowledge", load_in_4bit=True)
+```
+
+Training on it makes your model a Derivative under the Muse Model License too: keep "Built with Muse Spark Open", the
+license files and the Usage Policy with it. We have not tested these snippets as written.
+
+### With llama.cpp
+
+Tested with llama.cpp b11970. Use our reasoning-off chat template, `tuned-reasoning-off.jinja`, from this repo. It is
+modified from Meta's `chat_template.jinja`, and a comment at its top says what changed.
+
+```
+llama-server -m glimmer-stage-b-r30b-Q4_K_M.gguf \
+  --jinja --chat-template-file tuned-reasoning-off.jinja \
+  --reasoning-format none \
+  -c 4096
+```
+
+Then call the chat endpoint:
+
+```
+curl http://127.0.0.1:8080/v1/chat/completions -H 'Content-Type: application/json' -d '{
+  "messages": [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": "Which AI models came out in 2026?"}
+  ],
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "top_k": 64,
+  "stop": ["<|eot|>", "<|end_of_text|>"]
+}'
+```
+
+Why each part matters:
+
+- **The reasoning-off template.** It sends the reply straight to the user, with no reasoning channel. Questions about
+  facts should go this way. The model learned honesty only in direct answers (see Limitations).
+- **`--reasoning-format none`.** With the default (`auto`), llama.cpp's Glimmer parser rejects this model's replies
+  when reasoning is off.
+- **The stop list.** Stop on `<|eot|>` and `<|end_of_text|>`, never on `<|eom|>`. Without the stop field the server can
+  return HTTP 200 with markup in the reply, so a status check won't catch it.
+- **Sampling.** Meta's published settings for Glimmer are temperature 1.0, top_p 0.95, top_k 64. Our scores are greedy,
+  so served results will differ. llama.cpp also reads sampling defaults from the GGUF, so set these per request.
+- **A system message.** Send one. Without it, the template writes Meta's default "Knowledge cutoff: 2026-01-04" line
+  (see Limitations).
+- **Reasoning strength.** If you send none, the template writes "Reasoning strength: high." Training used low. Which
+  works better is under test. To match training, send `"chat_template_kwargs": {"reasoning_strength": "low"}`.
+
+Reasoning on is for reasoning tasks (math, multi-step problems), not for facts. The template turns it on with
+`"chat_template_kwargs": {"allow_reasoning": true}`. Keep `--reasoning-format none` and split the reasoning from the
+answer in your app. The template allows no tool calls while reasoning is off.
+
+## Limitations and known dangers
+
+The Usage Policies ask everyone who deploys this model to tell users its known dangers. These are the ones we know.
+Pass them on to your users.
+
+- **It makes things up.** On the held-out honesty test it made up an answer 55.3% of the time (base 70.0%). Don't rely
+  on it for facts without a source.
+- **It accepts false premises.** If a question assumes something false, it goes along with it about 80% of the time,
+  no better than base.
+- **It states wrong answers as fact.** On the 246 headline events it got 76 wrong: 66 of them stated as fact, 10
+  refused. On the 2,500, about 53.6% of its replies were wrong answers stated as fact (a rough automatic split).
+- **Reasoning on makes it less honest.** On our dev set (in-distribution, so both numbers are optimistic) it made up 14
+  of 120 answers with reasoning off, and 33 of 120 with reasoning on. With reasoning on, another 31 replies had an empty
+  answer. It learned honesty only in direct answers. Use reasoning off for facts.
+- **The 1-bit files loop.** The earlier r30 candidate's IQ1_S got stuck repeating itself on 10 of 60 longer replies.
+  Base IQ1_S loops on most replies. This release's loop check is pending. Set a max token limit.
+- **It refuses some things it knows.** It says "I don't know" to about 7% (7.2%) of the questions stage 2 answered
+  right, and it refuses 18.0% of ordinary control questions (base 6.0%).
+- **It knows only part of 2026.** 29% on reworded questions about passages it trained on. It will miss many events.
+- **Small files lose knowledge.** On the earlier r30 candidate's files, IQ2_XXS scored 16.7%, IQ1_M 9.5% and IQ1_S 8.4%
+  on the 2,500, against 26.2% for Q4_K_M, and the IQ1 files forgot some old facts. This release's small files are not
+  scored yet. In an earlier test, IQ1_S failed at reasoning on base and on the
+  stage 1 model alike.
+- **No vision test on the tuned files.** The vision encoder is Meta's, unchanged, but we never ran images through
+  these files. This release has no vision projector (mmproj) file.
+- **Tool calls are untested** on this release. No training row had tools.
+- **It reads its own cutoff line.** With no system message, the template tells the model "Knowledge cutoff:
+  2026-01-04", Meta's default, which is wrong for this model. The model may then refuse questions about later events.
+  Send a system message. Which system line works best is pending.
+- **Adults only.** Both usage policies say the models are not meant for people under 18.
+- **Don't present its outputs as written by a person.** Both usage policies bar this.
+- **Greedy numbers, one-author tests.** Every score here is greedy, and served results with sampling may differ. The
+  headline events test was written and graded by a single author.
+
+## License and data
+
+**Built with Muse Spark Open.**
+
+This is not an official Meta release. Meta has not reviewed or endorsed it. The names Muse, Muse Glimmer and Muse Spark
+appear here only to say what the model is built on.
+
+### License
+
+This model is a fine-tune of Muse Glimmer 30B (`meta-models/Muse-Glimmer-30B`, revision
+`cec94fcfd6512f2cfe708138c32533b346ea4f69`), which Meta releases under the Apache License 2.0.
+
+Part of the training data was written by Muse Spark Open. The Muse Model License counts any model trained on Muse Spark
+Open's outputs as a "Derivative". So this model is a Derivative of Muse Spark Open, and it carries two sets of terms:
+
+- the Apache License 2.0 (`LICENSE`), from the base model. We release our changes under Apache 2.0 too;
+- the Muse Model License for Muse Spark Open, with its Usage Policy as Exhibit A (`MUSE_MODEL_LICENSE.md`), and the
+  Muse Glimmer Usage Policy (`USAGE_POLICY.md`).
+
+If you use, copy or build on this model (the adapter, the GGUF files, a merge, or a further fine-tune), you must:
+
+1. Keep `LICENSE`, `MUSE_MODEL_LICENSE.md`, `USAGE_POLICY.md` and `NOTICE` with every copy you share.
+2. Show "Built with Muse Spark Open" in your product's interface, or in its documentation, website or settings menu.
+3. Follow both usage policies: Muse Glimmer's (`USAGE_POLICY.md`) and Muse Spark Open's (Exhibit A of
+   `MUSE_MODEL_LICENSE.md`). Both say the models are not meant for people under 18. Both bar presenting the model's
+   outputs as written by a person. Both ask you to tell your users the known dangers (see above).
+4. Keep every other condition of the Muse Model License. Its section 3: if you offer AI models to others as a service
+   through an API, and you and your affiliates take in more than 20 million US dollars in any 12 months, you must
+   notify Meta and sign a separate agreement with Meta first.
+
+What we changed: we trained LoRA weights (rank 64) on the language model's projection layers. The merged and quantized
+files include those changes. We did not change the vision encoder, the tokenizer or the model configuration. We also
+ship `tuned-reasoning-off.jinja`, modified from Meta's `chat_template.jinja`: it sends the reply straight to the user
+with no reasoning channel unless `allow_reasoning` is set, and it allows no tool calls while reasoning is off.
+
+The model comes with no warranty, as both sets of terms state.
+
+### Training data
+
+**How it was trained.** The model never trained on raw articles. Other models read short passages and wrote
+question-and-answer pairs about them, and the model trained on those pairs.
+
+| Pass | Examples | What |
+|---|---|---|
+| Stage 1 | 199,309 | One set of questions per passage, from about 43,000 passages |
+| Stage 2 | 520,895 | More questions on the same and new passages, mostly news about AI, Meta and the world |
+| Stage B | 60,000 | Honest "I don't know" answers, false-premise and after-cutoff questions, and reasoning examples |
+
+**Who wrote it.**
+
+- Muse Spark Open, run by hosting providers (Fireworks, Baseten, Modal): questions for about 31,800 passages, plus 400
+  overview answers. This is why the Muse Model License applies.
+- Muse Glimmer 30B, self-hosted: questions for about 183,600 passages, the Stage B honesty and reasoning rows, and 6,739
+  answers to prompts from Databricks Dolly 15k (CC BY-SA 3.0), which help it keep its general skills.
+- No outputs from Meta's hosted Model API (Muse Wonder, Muse Spark 1.x) and no search results from Exa were used.
+
+**Where the passages came from**, all published between January 4 and October 29, 2026:
+
+- News: articles from Common Crawl's CC-NEWS archive, picked for AI, Meta and general news. See "News and copyright"
+  below.
+- Wikipedia articles and Portal:Current events (CC BY-SA 4.0).
+- Government and public bodies: US federal sources, the UK's ONS and Parliament, the European Commission and EUR-Lex,
+  and others.
+- Meta's public docs, newsroom, engineering blog and cookbooks, and the PyTorch blog.
+- Software and AI: Hugging Face model cards under Apache 2.0 or MIT, release notes, models.dev, Epoch AI, LMArena.
+- Science and health: papers and preprints from PubMed Central, bioRxiv, medRxiv and OpenAlex, under each paper's own
+  license. See "Non-commercial papers and court records" below.
+
+**Left out on purpose:** Infini-News and Fine-News (research-only terms), Wikisource, US Department of Justice records,
+ForecastBench, Open Library, GDELT text, PubMed abstracts, Metaculus, FRED, and sites that opt out of AI training.
+
+**News and copyright.** About 82% of stage 2's examples are question-and-answer pairs written from CC-NEWS articles.
+The articles belong to their publishers. We never trained on the article text itself. We dropped sites that opt out of
+AI training (robots.txt rules for AI crawlers, TDMRep and `noai` signals), and we tried to drop wire-service and
+press-release copy, though some got through. The answers do reuse some wording: some answers written from CC-NEWS
+repeat long runs of words from their article. Publishers have open lawsuits against AI developers over
+training on news, and the law on this is not settled. We chose to publish the weights with this risk disclosed.
+
+**Non-commercial papers and court records.** Our own data rules were meant to leave both of these out, but they got
+into stage 1:
+
+- Examples written from science papers whose licenses are non-commercial (NC), no-derivatives (ND), or allow no
+  reuse. We release our changes under Apache 2.0 anyway, with this risk disclosed. Those papers' licenses are their
+  authors' terms. We can't waive them for you.
+- Examples written from US court records (CourtListener and the US Supreme Court). Court records can name private
+  people.
+
+**Fireworks terms.** Part of the Muse Spark Open data was generated through Fireworks' hosted service. Section 2.2(c)
+of Fireworks' terms limits using content available through its service for AI training. We have no written answer from
+Fireworks on whether that covers outputs we generated there. We chose to publish without waiting for one.
+
+**Personal data.** We removed passages that held email addresses, phone numbers, press contacts, or family and age
+details about people. Our news picks skipped personal profiles, obituaries and stories about minors. Filters miss
+things: the model may still know names from news and court cases. Don't use it to look up private people.
+
+**The training data is not in this repo.** This repo holds model files only.
+
+**Credits.** Wikipedia contributors (CC BY-SA 4.0). Databricks Dolly 15k (CC BY-SA 3.0). Contains public sector
+information licensed under the Open Government Licence v3.0 (Office for National Statistics). Contains Parliamentary
+information licensed under the Open Parliament Licence v3.0. European Commission and EUR-Lex, (c) European Union, 2026
+(CC BY 4.0). Global Voices (CC BY 3.0). kremlin.ru (CC BY 4.0). World Bank Pink Sheet and OECD (CC BY 4.0). Jonathan C.
+McDowell, GCAT (CC BY 4.0). Epoch AI and LMArena (CC BY 4.0). Cricsheet (ODC-BY 1.0). models.dev (MIT). Source: U.S.
+Energy Information Administration. BIS statistics; no endorsement by the BIS is implied. USDA NASS; not endorsed or
+certified by NASS. Courtesy of the U.S. National Library of Medicine; NLM does not endorse this product. NASA and the
+NASA Exoplanet Archive; no endorsement implied. Legifrance (Licence Ouverte 2.0), e-Gov Japan (PDL 1.0), GASTAT.
+Science papers under CC BY licenses: credit to their authors.
+
+## Contact and takedown
+
+If you hold rights in material you think this model reproduces, or you find personal data about you in its outputs,
+open a discussion in this repository's Community tab on Hugging Face. We will review every request.

USAGE_POLICY.md

@@ -0,0 +1,46 @@
+# Muse Glimmer Usage Policy
+
+Meta is committed to promoting safe and fair use of its tools and features, including Muse Glimmer. This Usage Policy (“Policy”) applies to your access or use of Muse Glimmer. The most recent copy of this policy can be found at: [https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/USAGE\_POLICY](https://huggingface.co/meta-models/Muse-Glimmer-30B/blob/main/USAGE_POLICY.md)
+
+## Prohibited Uses
+
+We want everyone to use Muse Glimmer safely and responsibly. Muse Glimmer is not intended for individuals under the age of 18\.
+
+You should not use, or allow others to use, Muse Glimmer to:
+
+1. **Violate the law or others' rights, including to:**
+   1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
+      1. Violence or terrorism
+      2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
+      3. Human trafficking, exploitation, and sexual violence
+      4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials
+      5. Sexual solicitation
+      6. Any other criminal activity
+   2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
+   3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
+   4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
+   5. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals' identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
+   6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using Muse Glimmer
+   7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
+   8. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta
+2. **Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Muse Glimmer related to the following:**
+   1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
+   2. Guns and illegal weapons (including weapon development)
+   3. Illegal drugs and regulated/controlled substances
+   4. Operation of critical infrastructure, transportation technologies, or heavy machinery
+   5. Self-harm or harm to others, including suicide, cutting, and eating disorders
+   6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
+3. **Intentionally deceive or mislead others, including use of Muse Glimmer related to the following:**
+   1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
+   2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
+   3. Generating, promoting, or further distributing spam
+   4. Impersonating another individual without consent, authorization, or legal right
+   5. Representing that the use of Muse Glimmer or outputs are human generated
+   6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
+4. **Fail to appropriately disclose to end users any known dangers of your AI system**
+5. **Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Muse Glimmer**
+
+Please report any violation of this Policy, software "bug," or other problems that could lead to a violation of this Policy through one of the following means:
+
+* Reporting issues with the model: [https://huggingface.co/meta-models/](https://huggingface.co/meta-models/)
+* Reporting bugs and security concerns: [https://facebook.com/whitehat/info](https://facebook.com/whitehat/info)

chat_template.jinja

@@ -0,0 +1,211 @@
+{#
+  Template: Muse Glimmer ATEM Chat Template
+  Renders the ATEM tool-calling protocol: reasoning channel (to=self), tool
+  channels (to=<tool>), and the user channel, plus tool definitions and the
+  valid-recipient list in the system block.
+
+  Whitespace note: every tag uses the {%- -%} / {{- -}} stripping markers, so
+  the indentation below is purely for readability and contributes nothing to
+  the rendered output.
+#}
+{%- macro render_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is not none -%}
+        {%- for part in content -%}
+            {%- if part['type'] == 'image' -%}
+                {{- '<|patch|>' -}}
+            {%- elif part['type'] == 'video' -%}
+                {{- '<|video|>' -}}
+            {%- elif part['type'] == 'text' -%}
+                {{- part['text'] -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro render_atem(tc) -%}
+    {%- set args = tc.function.arguments -%}
+    {%- if args is not mapping -%}
+        {{- raise_exception('Muse Glimmer ATEM chat template requires tool_call.function.arguments to be a dict (mapping); a JSON string cannot be parsed in the HF jinja sandbox.') -}}
+    {%- endif -%}
+    {{- '<atem:function_calls>\n<atem:invoke name="' + tc.function.name + '">\n' -}}
+    {%- for k, v in args.items() -%}
+        {{- '<atem:parameter name="' + k + '">' -}}
+        {%- if v is boolean -%}
+            {%- if v -%}
+                true
+            {%- else -%}
+                false
+            {%- endif -%}
+        {%- elif v is none -%}
+            null
+        {%- elif v is mapping or (v is iterable and v is not string) -%}
+            {{- v | tojson -}}
+        {%- else -%}
+            {{- v -}}
+        {%- endif -%}
+        {{- '</atem:parameter>\n' -}}
+    {%- endfor -%}
+    {{- '</atem:invoke>\n</atem:function_calls>' -}}
+{%- endmacro -%}
+{%- macro render_tool_defs(tools) -%}
+    {{- 'In this environment you have access to a set of tools you can use to answer the user\'s question.\n\n' -}}
+    {{- 'You can invoke a function by writing a "<atem:function_calls>" block like the following:\n' -}}
+    {{- '<atem:function_calls>\n<atem:invoke name="$FUNCTION_NAME">\n<atem:parameter name="$PARAMETER_NAME">$PARAMETER_VALUE</atem:parameter>\n...\n</atem:invoke>\n</atem:function_calls>\n\n' -}}
+    {{- 'String and scalar parameters should be specified as is, while lists and objects should use JSON format. Note that spaces for string values are not stripped. The output is not expected to be valid XML and is parsed with regular expressions.\n' -}}
+    {{- 'Here are the functions available in JSONSchema format:\n' -}}
+    {{- '// Tool metadata\n' -}}
+    {%- set nsns = namespace(seen=[]) -%}
+    {%- for tool in tools -%}
+        {%- set fn = tool.function if tool.function is defined else tool -%}
+        {%- set tns = fn.name.split('.')[0] -%}
+        {%- if tns not in nsns.seen -%}
+            {%- set nsns.seen = nsns.seen + [tns] -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set nd = tool_namespace_descriptions if tool_namespace_descriptions is defined else {} -%}
+    {%- for tns in nsns.seen -%}
+        {{- '{"name": ' + (tns | tojson) + ', "description": ' + ((nd[tns] if tns in nd else '') | tojson) + '}\n' -}}
+    {%- endfor -%}
+    {{- '// Function schemas' -}}
+    {%- for tool in tools -%}
+        {%- set fn = tool.function if tool.function is defined else tool -%}
+        {{- '\n{"name": ' + (fn.name | tojson) + ', "description": ' + (fn.description | tojson) + ', "parameters": ' + (fn.parameters | tojson) + '}' -}}
+    {%- endfor -%}
+    {{- '\n\nHere\'s an example of how to call a function in the tool set:\n' -}}
+    {{- '(If the tool namespace is not specified, invoke the function directly as `example_function_name` rather than `example_tool_name.example_function_name`)\n\n' -}}
+    {{- 'to=example_tool_name.example_function_name\n\n' -}}
+    {{- '<atem:function_calls>\n<atem:invoke name="example_tool_name.example_function_name">\n' -}}
+    {{- '<atem:parameter name="example_parameter_1">value_1</atem:parameter>\n' -}}
+    {{- '<atem:parameter name="example_parameter_2">This is the value for the second parameter\nthat can span\n"multiple" lines\n</atem:parameter>\n' -}}
+    {{- '</atem:invoke>\n</atem:function_calls>' -}}
+{%- endmacro -%}
+{%- macro render_reasoning() -%}
+    {%- set rs = reasoning_strength if reasoning_strength is defined and reasoning_strength else 'high' -%}
+    {{- 'Reasoning strength: ' + rs + '.' -}}
+{%- endmacro -%}
+{%- macro render_system_meta(tools) -%}
+    {%- set rns = namespace(recipients=['"self"'], nslist=[]) -%}
+    {%- if tools -%}
+        {%- for tool in tools -%}
+            {%- set fn = tool.function if tool.function is defined else tool -%}
+            {%- set tns = fn.name.split('.')[0] -%}
+            {%- if tns not in rns.nslist -%}
+                {%- set rns.nslist = rns.nslist + [tns] -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- for tns in rns.nslist -%}
+            {%- set rns.recipients = rns.recipients + ['"' + tns + '.*"'] -%}
+        {%- endfor -%}
+    {%- endif -%}
+    {%- set rns.recipients = rns.recipients + ['"user"'] -%}
+    {{- '# Valid recipients: ' + rns.recipients | join(', ') + '.' -}}
+{%- endmacro -%}
+{{- bos_token -}}
+{%- set ns = namespace(has_system=false) -%}
+{%- for m in messages -%}
+    {%- if m['role'] == 'system' -%}
+        {%- set ns.has_system = true -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- if not ns.has_system -%}
+    {{- '<|start|>system<|message|>You are a helpful AI assistant.' -}}
+    {%- set kc = knowledge_cutoff if knowledge_cutoff is defined and knowledge_cutoff else '2026-01-04' -%}
+    {{- '\nKnowledge cutoff: ' + kc + '.' -}}
+    {%- if current_date is defined and current_date -%}
+        {{- '\nCurrent date: ' + current_date + '.' -}}
+    {%- elif strftime_now is defined -%}
+        {{- '\nCurrent date: ' + strftime_now('%Y-%m-%d') + '.' -}}
+    {%- endif -%}
+    {{- '\n\n' -}}
+    {{- render_reasoning() -}}
+    {%- if tools -%}
+        {{- '\n\n' -}}
+        {{- render_tool_defs(tools) -}}
+    {%- endif -%}
+    {{- '\n\n' -}}
+    {{- render_system_meta(tools) -}}
+    {{- '<|eot|>' -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- set role = message['role'] -%}
+    {%- set end_token = '<|eom|>' if (not loop.last and messages[loop.index0 + 1]['role'] == role) else '<|eot|>' -%}
+    {%- if role == 'system' -%}
+        {#- Callers sometimes write the directive into the system prompt themselves.
+            Normalise "Reasoning effort" to "Reasoning strength" (jinja has no
+            case-insensitive replace, hence the four realistic casings), then skip
+            the kwarg-driven line below if the prompt already carries one. -#}
+        {%- set sys_text = render_content(message['content'])
+              | replace('Reasoning effort', 'Reasoning strength')
+              | replace('Reasoning Effort', 'Reasoning Strength')
+              | replace('reasoning effort', 'reasoning strength')
+              | replace('REASONING EFFORT', 'REASONING STRENGTH') -%}
+        {{- '<|start|>system<|message|>' -}}
+        {{- sys_text -}}
+        {%- if 'reasoning strength' not in (sys_text | lower) -%}
+            {{- '\n\n' -}}
+            {{- render_reasoning() -}}
+        {%- endif -%}
+        {%- if tools -%}
+            {{- '\n\n' -}}
+            {{- render_tool_defs(tools) -}}
+        {%- endif -%}
+        {{- '\n\n' -}}
+        {{- render_system_meta(tools) -}}
+        {{- '<|eot|>' -}}
+    {%- elif role == 'user' -%}
+        {{- '<|start|>user<|message|>' -}}
+        {{- render_content(message['content']) -}}
+        {{- '<|eot|>' -}}
+    {%- elif role == 'tool' -%}
+        {%- set tname = message.get('name') -%}
+        {%- if not tname -%}
+            {%- set tcid = message.get('tool_call_id') -%}
+            {%- set rns = namespace(name=tcid if tcid else '') -%}
+            {%- for m in messages -%}
+                {%- if m.get('tool_calls') -%}
+                    {%- for tc in m['tool_calls'] -%}
+                        {%- if tcid is not none and tc.id is defined and tc.id == tcid -%}
+                            {%- set rns.name = tc.function.name -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                {%- endif -%}
+            {%- endfor -%}
+            {%- set tname = rns.name -%}
+        {%- endif -%}
+        {{- '<|start|>tool ' + tname + '<|message|><tool_output name="' + tname + '">\n' -}}
+        {{- render_content(message['content']) -}}
+        {{- '\n</tool_output><|eot|>' -}}
+    {%- elif role == 'assistant' -%}
+        {%- if message.get('reasoning_content') -%}
+            {{- '<|start|>assistant to=self<|message|>' + message['reasoning_content'] + '<|eom|>' -}}
+        {%- endif -%}
+        {%- if message.get('tool_calls') -%}
+            {%- for tc in message['tool_calls'] -%}
+                {{- '<|start|>assistant to=' + tc.function.name + '<|message|>' -}}
+                {{- render_atem(tc) -}}
+                {%- if loop.last -%}
+                    {{- end_token -}}
+                {%- else -%}
+                    {{- '<|eom|>' -}}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- else -%}
+            {%- set recipient = message.get('recipient') or 'user' -%}
+            {%- set end_turn = message.get('end_turn') -%}
+            {%- if end_turn is none -%}
+                {%- set end_turn = not (recipient and recipient != 'user') -%}
+            {%- endif -%}
+            {{- '<|start|>assistant' -}}
+            {%- if recipient -%}
+                {{- ' to=' + recipient -}}
+            {%- endif -%}
+            {{- '<|message|>' -}}
+            {{- render_content(message['content']) -}}
+            {{- ('<|eot|>' if end_turn else '<|eom|>') -}}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- '<|start|>assistant' -}}
+{%- endif -%}

config.json

@@ -0,0 +1,222 @@
+{
+  "architectures": [
+    "MuseGlimmerForConditionalGeneration"
+  ],
+  "dtype": "bfloat16",
+  "image_token_id": 200092,
+  "model_type": "muse_glimmer",
+  "out_hidden_size": 6144,
+  "projector_hidden_act": "gelu",
+  "projector_hidden_size": 4096,
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 200000,
+    "eos_token_id": 200001,
+    "final_logit_softcapping": 20.0,
+    "head_dim": 128,
+    "hidden_activation": "silu",
+    "hidden_size": 6656,
+    "initializer_range": 0.02,
+    "intermediate_size": 19968,
+    "layer_rope_theta": [
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+    "layer_types": [
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "sliding_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 131072,
+    "model_type": "muse_glimmer_text",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 52,
+    "num_key_value_heads": 2,
+    "output_multiplier": 0.19611613513818404,
+    "pad_token_id": null,
+    "post_norm_eps": 1e-08,
+    "qk_scale_factor": 3.87,
+    "rms_norm_eps": 1e-05,
+    "rope_parameters": {
+      "rope_theta": 500000.0,
+      "rope_type": "default"
+    },
+    "sliding_window": 2048,
+    "tie_word_embeddings": false,
+    "use_cache": true,
+    "vocab_size": 202048
+  },
+  "transformers_version": "5.15.0.dev0",
+  "video_token_id": 200091,
+  "vision_config": {
+    "hidden_act": "gelu",
+    "hidden_size": 1536,
+    "intermediate_size": 8960,
+    "layer_norm_eps": 1e-05,
+    "layer_types": [
+      "window_attention",
+      "window_attention",
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+      "full_attention",
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+      "full_attention",
+      "window_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 1024,
+    "merge_size": 2,
+    "model_type": "muse_glimmer_vision",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 50,
+    "patch_size": 14,
+    "patch_temporal": 2,
+    "pos_emb_height": 32,
+    "pos_emb_width": 32,
+    "rope_parameters": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  }
+}

generation_config.json

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+  "bos_token_id": 200000,
+  "eos_token_id": [
+    200001,
+    200008
+  ],
+  "pad_token_id": 200018,
+  "max_length": 131072,
+  "do_sample": true,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "top_k": 64
+}

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processor_config.json

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+  "image_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+      0.5,
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+    "image_processor_type": "MuseGlimmerImageProcessor",
+    "image_std": [
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+    "max_image_tokens": 4096,
+    "merge_size": 2,
+    "patch_size": 14,
+    "resample": 1,
+    "rescale_factor": 0.00392156862745098,
+    "temporal_patch_size": 2
+  },
+  "processor_class": "MuseGlimmerProcessor",
+  "video_processor": {
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": true,
+    "fps": 2.0,
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+    "return_metadata": true,
+    "temporal_patch_size": 2,
+    "video_processor_type": "MuseGlimmerVideoProcessor"
+  }
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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "eos_token": "<|end_of_text|>",
+  "extra_special_tokens": [
+    "<|begin_of_text|>",
+    "<|end_of_text|>",
+    "<|reserved_special_token_2|>",
+    "<|reserved_special_token_3|>",
+    "<|reserved_special_token_4|>",
+    "<|reserved_special_token_5|>",
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+    "<|eom|>",
+    "<|eot|>",
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+    "<|reserved_special_token_20|>",
+    "<|reserved_special_token_21|>",
+    "<|start|>",
+    "<|message|>",
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+    "<|reserved_special_token_25|>",
+    "<|reserved_special_token_26|>",
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+    "<|reserved_special_token_78|>",
+    "<|reserved_special_token_79|>",
+    "<|image_start|>",
+    "<|image_end|>",
+    "<|vid_start|>",
+    "<|vid_end|>",
+    "<|reserved_special_token_84|>",
+    "<|reserved_special_token_85|>",
+    "<|reserved_special_token_86|>",
+    "<|vid_frame_separator|>",
+    "<|reserved_special_token_88|>",
+    "<|reserved_special_token_89|>",
+    "<|image|>",
+    "<|video|>",
+    "<|patch|>",
+    "<|reserved_special_token_93|>",
+    "<|reserved_special_token_94|>",
+    "<|reserved_special_token_95|>",
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+            "args": {
+              "allow_non_json": true
+            },
+            "name": "json"
+          }
+        },
+        "open_pattern": "<atem:invoke\\b[^>]*?\\bname=\"(?P<name>[^\"]+)\">",
+        "repeats": true,
+        "transform": {
+          "function": {
+            "arguments": "{content}",
+            "name": "{name}"
+          },
+          "type": "function"
+        }
+      }
+    },
+    "start_anchor": "<|start|>assistant"
+  },
+  "tokenizer_class": "TokenizersBackend"
+}

tuned-reasoning-off.jinja

@@ -0,0 +1,229 @@
+{#- tuned-reasoning-off.jinja
+    Modified from Meta's chat_template.jinja for Muse Glimmer 30B (meta-models/Muse-Glimmer-30B, revision
+    cec94fcfd6512f2cfe708138c32533b346ea4f69), which Meta releases under the Apache License 2.0 (see LICENSE and NOTICE).
+    What changed: unless chat_template_kwargs sets allow_reasoning to true, the generation prompt opens the reply to the
+    user (" to=user<|message|>"), so there is no reasoning channel and no tool calls on that turn. A comment block
+    explains this further down. Everything else is Meta's template as released.
+    Serve with llama.cpp --reasoning-format none and stop on <|eot|> and <|end_of_text|>, never <|eom|>.
+    Note: when the caller sends no reasoning strength, this template writes "Reasoning strength: high." The training rows
+    used low. Which works better is under test.
+    This is not an official Meta file and Meta does not endorse it. -#}
+{#
+  Template: Muse Glimmer ATEM Chat Template
+  Renders the ATEM tool-calling protocol: reasoning channel (to=self), tool
+  channels (to=<tool>), and the user channel, plus tool definitions and the
+  valid-recipient list in the system block.
+
+  Whitespace note: every tag uses the {%- -%} / {{- -}} stripping markers, so
+  the indentation below is purely for readability and contributes nothing to
+  the rendered output.
+#}
+{%- macro render_content(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is not none -%}
+        {%- for part in content -%}
+            {%- if part['type'] == 'image' -%}
+                {{- '<|patch|>' -}}
+            {%- elif part['type'] == 'video' -%}
+                {{- '<|video|>' -}}
+            {%- elif part['type'] == 'text' -%}
+                {{- part['text'] -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro render_atem(tc) -%}
+    {%- set args = tc.function.arguments -%}
+    {%- if args is not mapping -%}
+        {{- raise_exception('Muse Glimmer ATEM chat template requires tool_call.function.arguments to be a dict (mapping); a JSON string cannot be parsed in the HF jinja sandbox.') -}}
+    {%- endif -%}
+    {{- '<atem:function_calls>\n<atem:invoke name="' + tc.function.name + '">\n' -}}
+    {%- for k, v in args.items() -%}
+        {{- '<atem:parameter name="' + k + '">' -}}
+        {%- if v is boolean -%}
+            {%- if v -%}
+                true
+            {%- else -%}
+                false
+            {%- endif -%}
+        {%- elif v is none -%}
+            null
+        {%- elif v is mapping or (v is iterable and v is not string) -%}
+            {{- v | tojson -}}
+        {%- else -%}
+            {{- v -}}
+        {%- endif -%}
+        {{- '</atem:parameter>\n' -}}
+    {%- endfor -%}
+    {{- '</atem:invoke>\n</atem:function_calls>' -}}
+{%- endmacro -%}
+{%- macro render_tool_defs(tools) -%}
+    {{- 'In this environment you have access to a set of tools you can use to answer the user\'s question.\n\n' -}}
+    {{- 'You can invoke a function by writing a "<atem:function_calls>" block like the following:\n' -}}
+    {{- '<atem:function_calls>\n<atem:invoke name="$FUNCTION_NAME">\n<atem:parameter name="$PARAMETER_NAME">$PARAMETER_VALUE</atem:parameter>\n...\n</atem:invoke>\n</atem:function_calls>\n\n' -}}
+    {{- 'String and scalar parameters should be specified as is, while lists and objects should use JSON format. Note that spaces for string values are not stripped. The output is not expected to be valid XML and is parsed with regular expressions.\n' -}}
+    {{- 'Here are the functions available in JSONSchema format:\n' -}}
+    {{- '// Tool metadata\n' -}}
+    {%- set nsns = namespace(seen=[]) -%}
+    {%- for tool in tools -%}
+        {%- set fn = tool.function if tool.function is defined else tool -%}
+        {%- set tns = fn.name.split('.')[0] -%}
+        {%- if tns not in nsns.seen -%}
+            {%- set nsns.seen = nsns.seen + [tns] -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set nd = tool_namespace_descriptions if tool_namespace_descriptions is defined else {} -%}
+    {%- for tns in nsns.seen -%}
+        {{- '{"name": ' + (tns | tojson) + ', "description": ' + ((nd[tns] if tns in nd else '') | tojson) + '}\n' -}}
+    {%- endfor -%}
+    {{- '// Function schemas' -}}
+    {%- for tool in tools -%}
+        {%- set fn = tool.function if tool.function is defined else tool -%}
+        {{- '\n{"name": ' + (fn.name | tojson) + ', "description": ' + (fn.description | tojson) + ', "parameters": ' + (fn.parameters | tojson) + '}' -}}
+    {%- endfor -%}
+    {{- '\n\nHere\'s an example of how to call a function in the tool set:\n' -}}
+    {{- '(If the tool namespace is not specified, invoke the function directly as `example_function_name` rather than `example_tool_name.example_function_name`)\n\n' -}}
+    {{- 'to=example_tool_name.example_function_name\n\n' -}}
+    {{- '<atem:function_calls>\n<atem:invoke name="example_tool_name.example_function_name">\n' -}}
+    {{- '<atem:parameter name="example_parameter_1">value_1</atem:parameter>\n' -}}
+    {{- '<atem:parameter name="example_parameter_2">This is the value for the second parameter\nthat can span\n"multiple" lines\n</atem:parameter>\n' -}}
+    {{- '</atem:invoke>\n</atem:function_calls>' -}}
+{%- endmacro -%}
+{%- macro render_reasoning() -%}
+    {%- set rs = reasoning_strength if reasoning_strength is defined and reasoning_strength else 'high' -%}
+    {{- 'Reasoning strength: ' + rs + '.' -}}
+{%- endmacro -%}
+{%- macro render_system_meta(tools) -%}
+    {%- set rns = namespace(recipients=['"self"'], nslist=[]) -%}
+    {%- if tools -%}
+        {%- for tool in tools -%}
+            {%- set fn = tool.function if tool.function is defined else tool -%}
+            {%- set tns = fn.name.split('.')[0] -%}
+            {%- if tns not in rns.nslist -%}
+                {%- set rns.nslist = rns.nslist + [tns] -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- for tns in rns.nslist -%}
+            {%- set rns.recipients = rns.recipients + ['"' + tns + '.*"'] -%}
+        {%- endfor -%}
+    {%- endif -%}
+    {%- set rns.recipients = rns.recipients + ['"user"'] -%}
+    {{- '# Valid recipients: ' + rns.recipients | join(', ') + '.' -}}
+{%- endmacro -%}
+{{- bos_token -}}
+{%- set ns = namespace(has_system=false) -%}
+{%- for m in messages -%}
+    {%- if m['role'] == 'system' -%}
+        {%- set ns.has_system = true -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- if not ns.has_system -%}
+    {{- '<|start|>system<|message|>You are a helpful AI assistant.' -}}
+    {%- set kc = knowledge_cutoff if knowledge_cutoff is defined and knowledge_cutoff else '2026-01-04' -%}
+    {{- '\nKnowledge cutoff: ' + kc + '.' -}}
+    {%- if current_date is defined and current_date -%}
+        {{- '\nCurrent date: ' + current_date + '.' -}}
+    {%- elif strftime_now is defined -%}
+        {{- '\nCurrent date: ' + strftime_now('%Y-%m-%d') + '.' -}}
+    {%- endif -%}
+    {{- '\n\n' -}}
+    {{- render_reasoning() -}}
+    {%- if tools -%}
+        {{- '\n\n' -}}
+        {{- render_tool_defs(tools) -}}
+    {%- endif -%}
+    {{- '\n\n' -}}
+    {{- render_system_meta(tools) -}}
+    {{- '<|eot|>' -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- set role = message['role'] -%}
+    {%- set end_token = '<|eom|>' if (not loop.last and messages[loop.index0 + 1]['role'] == role) else '<|eot|>' -%}
+    {%- if role == 'system' -%}
+        {#- Callers sometimes write the directive into the system prompt themselves.
+            Normalise "Reasoning effort" to "Reasoning strength" (jinja has no
+            case-insensitive replace, hence the four realistic casings), then skip
+            the kwarg-driven line below if the prompt already carries one. -#}
+        {%- set sys_text = render_content(message['content'])
+              | replace('Reasoning effort', 'Reasoning strength')
+              | replace('Reasoning Effort', 'Reasoning Strength')
+              | replace('reasoning effort', 'reasoning strength')
+              | replace('REASONING EFFORT', 'REASONING STRENGTH') -%}
+        {{- '<|start|>system<|message|>' -}}
+        {{- sys_text -}}
+        {%- if 'reasoning strength' not in (sys_text | lower) -%}
+            {{- '\n\n' -}}
+            {{- render_reasoning() -}}
+        {%- endif -%}
+        {%- if tools -%}
+            {{- '\n\n' -}}
+            {{- render_tool_defs(tools) -}}
+        {%- endif -%}
+        {{- '\n\n' -}}
+        {{- render_system_meta(tools) -}}
+        {{- '<|eot|>' -}}
+    {%- elif role == 'user' -%}
+        {{- '<|start|>user<|message|>' -}}
+        {{- render_content(message['content']) -}}
+        {{- '<|eot|>' -}}
+    {%- elif role == 'tool' -%}
+        {%- set tname = message.get('name') -%}
+        {%- if not tname -%}
+            {%- set tcid = message.get('tool_call_id') -%}
+            {%- set rns = namespace(name=tcid if tcid else '') -%}
+            {%- for m in messages -%}
+                {%- if m.get('tool_calls') -%}
+                    {%- for tc in m['tool_calls'] -%}
+                        {%- if tcid is not none and tc.id is defined and tc.id == tcid -%}
+                            {%- set rns.name = tc.function.name -%}
+                        {%- endif -%}
+                    {%- endfor -%}
+                {%- endif -%}
+            {%- endfor -%}
+            {%- set tname = rns.name -%}
+        {%- endif -%}
+        {{- '<|start|>tool ' + tname + '<|message|><tool_output name="' + tname + '">\n' -}}
+        {{- render_content(message['content']) -}}
+        {{- '\n</tool_output><|eot|>' -}}
+    {%- elif role == 'assistant' -%}
+        {%- if message.get('reasoning_content') -%}
+            {{- '<|start|>assistant to=self<|message|>' + message['reasoning_content'] + '<|eom|>' -}}
+        {%- endif -%}
+        {%- if message.get('tool_calls') -%}
+            {%- for tc in message['tool_calls'] -%}
+                {{- '<|start|>assistant to=' + tc.function.name + '<|message|>' -}}
+                {{- render_atem(tc) -}}
+                {%- if loop.last -%}
+                    {{- end_token -}}
+                {%- else -%}
+                    {{- '<|eom|>' -}}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- else -%}
+            {%- set recipient = message.get('recipient') or 'user' -%}
+            {%- set end_turn = message.get('end_turn') -%}
+            {%- if end_turn is none -%}
+                {%- set end_turn = not (recipient and recipient != 'user') -%}
+            {%- endif -%}
+            {{- '<|start|>assistant' -}}
+            {%- if recipient -%}
+                {{- ' to=' + recipient -}}
+            {%- endif -%}
+            {{- '<|message|>' -}}
+            {{- render_content(message['content']) -}}
+            {{- ('<|eot|>' if end_turn else '<|eom|>') -}}
+        {%- endif -%}
+    {%- endif -%}
+{%- endfor -%}
+{#- Tuned Muse Glimmer serving: reasoning off by default. The generation prompt opens the final channel, so the reply is the
+    answer. Needs llama-server --reasoning-format none: llama.cpp's Muse Glimmer parser (common/parsers/muse-glimmer.cpp) expects
+    to read the channel header in the reply and returns HTTP 500 without it. Send "stop": ["<|eot|>"], since with that format the
+    end token stays in the text. For the base model's own generation prompt on one request, send chat_template_kwargs
+    {"allow_reasoning": true} and "reasoning_format": "auto". No tool calls while reasoning is off: the turn already goes to the user. -#}
+{%- if add_generation_prompt -%}
+    {{- '<|start|>assistant' -}}
+    {%- if not (allow_reasoning is defined and allow_reasoning) -%}
+        {{- ' to=user<|message|>' -}}
+    {%- endif -%}
+{%- endif -%}