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Muse-Glimmer-30B-MLX-4bit

Image-Text-to-Text
MLX
Safetensors
English
muse_glimmer
mlx-vlm
omlx
muse-glimmer
multimodal
vision-language
quantized
apple-silicon
4-bit precision
conversational
Model card Files
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Publish validated Muse-Glimmer-30B-MLX-4bit MLX checkpoint

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  1. .gitattributes +1 -0
  2. LICENSE +202 -0
  3. README.md +205 -0
  4. USAGE_POLICY.md +46 -0
  5. chat_template.jinja +211 -0
  6. config.json +253 -0
  7. generation_config.json +13 -0
  8. model-00001-of-00004.safetensors +3 -0
  9. model-00002-of-00004.safetensors +3 -0
  10. model-00003-of-00004.safetensors +3 -0
  11. model-00004-of-00004.safetensors +3 -0
  12. model.safetensors.index.json +0 -0
  13. processor_config.json +21 -0
  14. tokenizer.json +3 -0
  15. tokenizer_config.json +2104 -0
.gitattributes CHANGED
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
LICENSE ADDED
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README.md ADDED
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1
+ ---
2
+ library_name: mlx
3
+ license: apache-2.0
4
+ base_model: meta-models/Muse-Glimmer-30B
5
+ base_model_relation: quantized
6
+ pipeline_tag: image-text-to-text
7
+ language:
8
+ - en
9
+ tags:
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+ - mlx
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+ - mlx-vlm
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+ - omlx
13
+ - muse-glimmer
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+ - multimodal
15
+ - vision-language
16
+ - quantized
17
+ - apple-silicon
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+ - 4-bit
19
+ ---
20
+
21
+ <p align="center">
22
+ <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" width="92" alt="Hugging Face logo">
23
+ </p>
24
+
25
+ <p align="center">
26
+ <img src="https://img.shields.io/badge/Meta-Muse_Glimmer-0467DF?style=for-the-badge&logo=meta&logoColor=white" alt="Meta Muse Glimmer">
27
+ <img src="https://img.shields.io/badge/Apple_Silicon-MLX-000000?style=for-the-badge&logo=apple&logoColor=white" alt="Apple silicon MLX">
28
+ <img src="https://img.shields.io/badge/Vontra-MLX_VLM-6E56CF?style=for-the-badge&logo=huggingface&logoColor=white" alt="Vontra MLX VLM">
29
+ </p>
30
+
31
+ <h1 align="center">Muse Glimmer 30B — MLX 4-bit</h1>
32
+
33
+ <p align="center">
34
+ A native Apple-silicon conversion of <a href="https://huggingface.co/meta-models/Muse-Glimmer-30B">meta-models/Muse-Glimmer-30B</a>, converted to uniform affine 4-bit MLX weights for text-and-image inference with MLX-VLM.
35
+ </p>
36
+
37
+ <p align="center">
38
+ <a href="https://huggingface.co/meta-models/Muse-Glimmer-30B">Original model</a> ·
39
+ <a href="https://github.com/Blaizzy/mlx-vlm">MLX-VLM</a> ·
40
+ <a href="https://github.com/ml-explore/mlx">MLX</a> ·
41
+ <a href="https://huggingface.co/Vontra">More Vontra conversions</a>
42
+ </p>
43
+
44
+ ## About this conversion
45
+
46
+ This repository contains a **uniform MLX 4-bit conversion** of Muse Glimmer 30B, a dense agentic language model with a dedicated perception encoder. It preserves the upstream tokenizer, ATEM chat template, image processor configuration, generation configuration, licence, and usage policy.
47
+
48
+ | Item | Value |
49
+ | --- | --- |
50
+ | Base model | [`meta-models/Muse-Glimmer-30B`](https://huggingface.co/meta-models/Muse-Glimmer-30B) |
51
+ | Format | MLX safetensors |
52
+ | Quantization | Affine 4-bit for eligible modules, group size 64 |
53
+ | Mixed-precision overrides | None |
54
+ | Conversion/runtime stack | MLX-VLM with MLX on Apple silicon |
55
+ | Weight shards | 4 |
56
+ | Weight size | 19.41 GB (18.08 GiB) |
57
+ | Context length | 131,072 tokens |
58
+ | Maximum visual tokens | 4,096 per image |
59
+ | Architecture | `muse_glimmer` |
60
+
61
+ Modules that MLX-VLM does not classify as quantizable remain at their source-compatible precision, so the effective whole-checkpoint bits-per-weight is higher than four. The `quantization` metadata records the actual eligible-module recipe.
62
+
63
+ > [!IMPORTANT]
64
+ > Muse Glimmer support was validated against the official MLX-VLM source at commit [`5262cb6`](https://github.com/Blaizzy/mlx-vlm/commit/5262cb6a27c797c5c3daf64b17d757924c5c474f), reporting package version 0.6.12. An older MLX-VLM or oMLX bundle may report `Model type muse_glimmer not supported`; update to a build containing the upstream Muse implementation before loading this checkpoint.
65
+
66
+ ## Apple-silicon performance
67
+
68
+ This checkpoint was load-tested, text-generation tested, and benchmarked on:
69
+
70
+ | Hardware | Configuration |
71
+ | --- | --- |
72
+ | Host | Mac Studio |
73
+ | Chip | Apple M3 Ultra |
74
+ | CPU | 32 cores (24 performance + 8 efficiency) |
75
+ | Unified memory | 256 GB |
76
+ | Runtime | MLX-VLM 0.6.12 source revision `5262cb6` with the oMLX MLX runtime |
77
+
78
+ A warmed local text-only test produced:
79
+
80
+ | Measurement | Result |
81
+ | --- | ---: |
82
+ | Decode (median) | **37.70 tokens/s** |
83
+ | Individual decode runs | 37.70 / 37.58 / 38.19 tokens/s |
84
+ | Reported peak memory | **21.33 GB** |
85
+ | Timed runs | 3 × 256 generated tokens |
86
+ | Warm-up | 256 generated tokens |
87
+ | Prompt | 83 tokens after chat templating |
88
+
89
+ The decode figure is the median of three greedy 256-token runs after a full 256-token Metal-kernel warm-up. It is a practical local reference, not a controlled cross-platform benchmark. Prompt length, images, context growth, sampler settings, memory pressure, thermal state, and runtime versions can materially change performance. Image prefill is not included in this decode benchmark.
90
+
91
+ ## Runtime setup
92
+
93
+ Until Muse Glimmer support reaches the MLX-VLM build supplied by your application, install the exact official source revision used for validation:
94
+
95
+ ```bash
96
+ python -m pip install \
97
+ "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git@5262cb6a27c797c5c3daf64b17d757924c5c474f"
98
+ ```
99
+
100
+ Download the checkpoint if a local copy is preferred:
101
+
102
+ ```bash
103
+ hf download Vontra/Muse-Glimmer-30B-MLX-4bit \
104
+ --local-dir ~/.omlx/models/Vontra/Muse-Glimmer-30B-MLX-4bit
105
+ ```
106
+
107
+ ## Text generation
108
+
109
+ ```python
110
+ from mlx_vlm import generate, load
111
+
112
+ model, processor = load("Vontra/Muse-Glimmer-30B-MLX-4bit")
113
+ tokenizer = processor.tokenizer if hasattr(processor, "tokenizer") else processor
114
+ messages = [
115
+ {"role": "user", "content": "Explain unified memory on Apple silicon."}
116
+ ]
117
+ prompt = tokenizer.apply_chat_template(
118
+ messages,
119
+ tokenize=False,
120
+ add_generation_prompt=True,
121
+ reasoning_strength="medium",
122
+ )
123
+ result = generate(
124
+ model,
125
+ processor,
126
+ prompt,
127
+ max_tokens=512,
128
+ temperature=0.0,
129
+ )
130
+ print(result.text)
131
+ ```
132
+
133
+ Muse Glimmer supports `low`, `medium`, `high`, and `xhigh` reasoning strengths. Higher settings can spend more tokens reasoning before returning the final answer.
134
+
135
+ ## Image and text generation
136
+
137
+ The multimodal path was smoke-tested locally with a real image. Include an image content item so the ATEM template emits the required patch token:
138
+
139
+ ```python
140
+ from mlx_vlm import generate, load
141
+
142
+ model, processor = load("Vontra/Muse-Glimmer-30B-MLX-4bit")
143
+ tokenizer = processor.tokenizer if hasattr(processor, "tokenizer") else processor
144
+ messages = [
145
+ {
146
+ "role": "user",
147
+ "content": [
148
+ {"type": "image"},
149
+ {"type": "text", "text": "Describe this image in one sentence."},
150
+ ],
151
+ }
152
+ ]
153
+ prompt = tokenizer.apply_chat_template(
154
+ messages,
155
+ tokenize=False,
156
+ add_generation_prompt=True,
157
+ )
158
+ result = generate(
159
+ model,
160
+ processor,
161
+ prompt,
162
+ image=["image.jpg"],
163
+ max_tokens=256,
164
+ temperature=0.0,
165
+ )
166
+ print(result.text)
167
+ ```
168
+
169
+ ## Architecture
170
+
171
+ Muse Glimmer combines a dense causal transformer with a dedicated perception encoder for interleaved text and image input.
172
+
173
+ | Architecture detail | Upstream value |
174
+ | --- | ---: |
175
+ | Total parameters | ~29.6B |
176
+ | Transformer layers | 52 |
177
+ | Hidden size | 6,656 |
178
+ | Attention heads / KV heads | 32 / 2 |
179
+ | Head dimension | 128 |
180
+ | FFN intermediate size | 19,968 |
181
+ | Attention pattern | local / local / local / global |
182
+ | Sliding window | 2,048 tokens |
183
+ | Vocabulary size | 202,048 |
184
+ | Perception encoder | ~1.8B-parameter ViT-G/14, 50 layers |
185
+ | Context length | 131,072 tokens |
186
+
187
+ The model supports agentic task completion, tool use through the upstream ATEM protocol, controllable reasoning effort, multilingual input, failure recovery, and multimodal understanding. See the [original model card](https://huggingface.co/meta-models/Muse-Glimmer-30B) for upstream benchmarks, training details, intended uses, limitations, and safety guidance.
188
+
189
+ ## Conversion and validation notes
190
+
191
+ - Source weights: upstream BF16 checkpoint at revision `a4e59da52a7bc87ae7251dd5545c0dd437c44b68`.
192
+ - Quantization mode: affine, 4-bit, group size 64, without mixed-precision overrides.
193
+ - The upstream tokenizer, ATEM chat template, processor configuration, generation configuration, licence, and usage policy are included.
194
+ - All 2,278 converted tensors and all 4 indexed shards were checked locally.
195
+ - The checkpoint was exercised through full text generation; the Muse architecture was also smoke-tested with image input.
196
+ - Quantization can reduce output quality relative to BF16. Use a higher-precision variant when quality matters more than memory use.
197
+ - This release does not include or claim support for the upstream speculative drafter.
198
+
199
+ This is a community conversion, not an official Meta release. Validate quality, safety, and numerical behaviour on representative workloads before production use.
200
+
201
+ ## Licence, usage policy, and attribution
202
+
203
+ The upstream model is released under the **Apache License 2.0**. The upstream `LICENSE` and `USAGE_POLICY.md` files are included in this repository; use is subject to both the licence and the upstream usage policy.
204
+
205
+ All model design, training, benchmark, and upstream documentation credit belongs to Meta and the original contributors. The MLX conversion, Apple-silicon validation, compatibility work, and model card are provided by [Vontra](https://huggingface.co/Vontra).
USAGE_POLICY.md ADDED
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1
+ # Muse Glimmer Usage Policy
2
+
3
+ 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)
4
+
5
+ ## Prohibited Uses
6
+
7
+ We want everyone to use Muse Glimmer safely and responsibly. Muse Glimmer is not intended for individuals under the age of 18\.
8
+
9
+ You should not use, or allow others to use, Muse Glimmer to:
10
+
11
+ 1. **Violate the law or others' rights, including to:**
12
+ 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
13
+ 1. Violence or terrorism
14
+ 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
15
+ 3. Human trafficking, exploitation, and sexual violence
16
+ 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
17
+ 5. Sexual solicitation
18
+ 6. Any other criminal activity
19
+ 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
20
+ 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
21
+ 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
22
+ 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
23
+ 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
24
+ 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
25
+ 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
26
+ 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:**
27
+ 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
28
+ 2. Guns and illegal weapons (including weapon development)
29
+ 3. Illegal drugs and regulated/controlled substances
30
+ 4. Operation of critical infrastructure, transportation technologies, or heavy machinery
31
+ 5. Self-harm or harm to others, including suicide, cutting, and eating disorders
32
+ 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
33
+ 3. **Intentionally deceive or mislead others, including use of Muse Glimmer related to the following:**
34
+ 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
35
+ 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
36
+ 3. Generating, promoting, or further distributing spam
37
+ 4. Impersonating another individual without consent, authorization, or legal right
38
+ 5. Representing that the use of Muse Glimmer or outputs are human generated
39
+ 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
40
+ 4. **Fail to appropriately disclose to end users any known dangers of your AI system**
41
+ 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**
42
+
43
+ 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:
44
+
45
+ * Reporting issues with the model: [https://huggingface.co/meta-models/](https://huggingface.co/meta-models/)
46
+ * Reporting bugs and security concerns: [https://facebook.com/whitehat/info](https://facebook.com/whitehat/info)
chat_template.jinja ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {#
2
+ Template: Muse Glimmer ATEM Chat Template
3
+ Renders the ATEM tool-calling protocol: reasoning channel (to=self), tool
4
+ channels (to=<tool>), and the user channel, plus tool definitions and the
5
+ valid-recipient list in the system block.
6
+
7
+ Whitespace note: every tag uses the {%- -%} / {{- -}} stripping markers, so
8
+ the indentation below is purely for readability and contributes nothing to
9
+ the rendered output.
10
+ #}
11
+ {%- macro render_content(content) -%}
12
+ {%- if content is string -%}
13
+ {{- content -}}
14
+ {%- elif content is not none -%}
15
+ {%- for part in content -%}
16
+ {%- if part['type'] == 'image' -%}
17
+ {{- '<|patch|>' -}}
18
+ {%- elif part['type'] == 'video' -%}
19
+ {{- '<|video|>' -}}
20
+ {%- elif part['type'] == 'text' -%}
21
+ {{- part['text'] -}}
22
+ {%- endif -%}
23
+ {%- endfor -%}
24
+ {%- endif -%}
25
+ {%- endmacro -%}
26
+ {%- macro render_atem(tc) -%}
27
+ {%- set args = tc.function.arguments -%}
28
+ {%- if args is not mapping -%}
29
+ {{- 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.') -}}
30
+ {%- endif -%}
31
+ {{- '<atem:function_calls>\n<atem:invoke name="' + tc.function.name + '">\n' -}}
32
+ {%- for k, v in args.items() -%}
33
+ {{- '<atem:parameter name="' + k + '">' -}}
34
+ {%- if v is boolean -%}
35
+ {%- if v -%}
36
+ true
37
+ {%- else -%}
38
+ false
39
+ {%- endif -%}
40
+ {%- elif v is none -%}
41
+ null
42
+ {%- elif v is mapping or (v is iterable and v is not string) -%}
43
+ {{- v | tojson -}}
44
+ {%- else -%}
45
+ {{- v -}}
46
+ {%- endif -%}
47
+ {{- '</atem:parameter>\n' -}}
48
+ {%- endfor -%}
49
+ {{- '</atem:invoke>\n</atem:function_calls>' -}}
50
+ {%- endmacro -%}
51
+ {%- macro render_tool_defs(tools) -%}
52
+ {{- 'In this environment you have access to a set of tools you can use to answer the user\'s question.\n\n' -}}
53
+ {{- 'You can invoke a function by writing a "<atem:function_calls>" block like the following:\n' -}}
54
+ {{- '<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' -}}
55
+ {{- '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' -}}
56
+ {{- 'Here are the functions available in JSONSchema format:\n' -}}
57
+ {{- '// Tool metadata\n' -}}
58
+ {%- set nsns = namespace(seen=[]) -%}
59
+ {%- for tool in tools -%}
60
+ {%- set fn = tool.function if tool.function is defined else tool -%}
61
+ {%- set tns = fn.name.split('.')[0] -%}
62
+ {%- if tns not in nsns.seen -%}
63
+ {%- set nsns.seen = nsns.seen + [tns] -%}
64
+ {%- endif -%}
65
+ {%- endfor -%}
66
+ {%- set nd = tool_namespace_descriptions if tool_namespace_descriptions is defined else {} -%}
67
+ {%- for tns in nsns.seen -%}
68
+ {{- '{"name": ' + (tns | tojson) + ', "description": ' + ((nd[tns] if tns in nd else '') | tojson) + '}\n' -}}
69
+ {%- endfor -%}
70
+ {{- '// Function schemas' -}}
71
+ {%- for tool in tools -%}
72
+ {%- set fn = tool.function if tool.function is defined else tool -%}
73
+ {{- '\n{"name": ' + (fn.name | tojson) + ', "description": ' + (fn.description | tojson) + ', "parameters": ' + (fn.parameters | tojson) + '}' -}}
74
+ {%- endfor -%}
75
+ {{- '\n\nHere\'s an example of how to call a function in the tool set:\n' -}}
76
+ {{- '(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' -}}
77
+ {{- 'to=example_tool_name.example_function_name\n\n' -}}
78
+ {{- '<atem:function_calls>\n<atem:invoke name="example_tool_name.example_function_name">\n' -}}
79
+ {{- '<atem:parameter name="example_parameter_1">value_1</atem:parameter>\n' -}}
80
+ {{- '<atem:parameter name="example_parameter_2">This is the value for the second parameter\nthat can span\n"multiple" lines\n</atem:parameter>\n' -}}
81
+ {{- '</atem:invoke>\n</atem:function_calls>' -}}
82
+ {%- endmacro -%}
83
+ {%- macro render_reasoning() -%}
84
+ {%- set rs = reasoning_strength if reasoning_strength is defined and reasoning_strength else 'high' -%}
85
+ {{- 'Reasoning strength: ' + rs + '.' -}}
86
+ {%- endmacro -%}
87
+ {%- macro render_system_meta(tools) -%}
88
+ {%- set rns = namespace(recipients=['"self"'], nslist=[]) -%}
89
+ {%- if tools -%}
90
+ {%- for tool in tools -%}
91
+ {%- set fn = tool.function if tool.function is defined else tool -%}
92
+ {%- set tns = fn.name.split('.')[0] -%}
93
+ {%- if tns not in rns.nslist -%}
94
+ {%- set rns.nslist = rns.nslist + [tns] -%}
95
+ {%- endif -%}
96
+ {%- endfor -%}
97
+ {%- for tns in rns.nslist -%}
98
+ {%- set rns.recipients = rns.recipients + ['"' + tns + '.*"'] -%}
99
+ {%- endfor -%}
100
+ {%- endif -%}
101
+ {%- set rns.recipients = rns.recipients + ['"user"'] -%}
102
+ {{- '# Valid recipients: ' + rns.recipients | join(', ') + '.' -}}
103
+ {%- endmacro -%}
104
+ {{- bos_token -}}
105
+ {%- set ns = namespace(has_system=false) -%}
106
+ {%- for m in messages -%}
107
+ {%- if m['role'] == 'system' -%}
108
+ {%- set ns.has_system = true -%}
109
+ {%- endif -%}
110
+ {%- endfor -%}
111
+ {%- if not ns.has_system -%}
112
+ {{- '<|start|>system<|message|>You are a helpful AI assistant.' -}}
113
+ {%- set kc = knowledge_cutoff if knowledge_cutoff is defined and knowledge_cutoff else '2026-01-04' -%}
114
+ {{- '\nKnowledge cutoff: ' + kc + '.' -}}
115
+ {%- if current_date is defined and current_date -%}
116
+ {{- '\nCurrent date: ' + current_date + '.' -}}
117
+ {%- elif strftime_now is defined -%}
118
+ {{- '\nCurrent date: ' + strftime_now('%Y-%m-%d') + '.' -}}
119
+ {%- endif -%}
120
+ {{- '\n\n' -}}
121
+ {{- render_reasoning() -}}
122
+ {%- if tools -%}
123
+ {{- '\n\n' -}}
124
+ {{- render_tool_defs(tools) -}}
125
+ {%- endif -%}
126
+ {{- '\n\n' -}}
127
+ {{- render_system_meta(tools) -}}
128
+ {{- '<|eot|>' -}}
129
+ {%- endif -%}
130
+ {%- for message in messages -%}
131
+ {%- set role = message['role'] -%}
132
+ {%- set end_token = '<|eom|>' if (not loop.last and messages[loop.index0 + 1]['role'] == role) else '<|eot|>' -%}
133
+ {%- if role == 'system' -%}
134
+ {#- Callers sometimes write the directive into the system prompt themselves.
135
+ Normalise "Reasoning effort" to "Reasoning strength" (jinja has no
136
+ case-insensitive replace, hence the four realistic casings), then skip
137
+ the kwarg-driven line below if the prompt already carries one. -#}
138
+ {%- set sys_text = render_content(message['content'])
139
+ | replace('Reasoning effort', 'Reasoning strength')
140
+ | replace('Reasoning Effort', 'Reasoning Strength')
141
+ | replace('reasoning effort', 'reasoning strength')
142
+ | replace('REASONING EFFORT', 'REASONING STRENGTH') -%}
143
+ {{- '<|start|>system<|message|>' -}}
144
+ {{- sys_text -}}
145
+ {%- if 'reasoning strength' not in (sys_text | lower) -%}
146
+ {{- '\n\n' -}}
147
+ {{- render_reasoning() -}}
148
+ {%- endif -%}
149
+ {%- if tools -%}
150
+ {{- '\n\n' -}}
151
+ {{- render_tool_defs(tools) -}}
152
+ {%- endif -%}
153
+ {{- '\n\n' -}}
154
+ {{- render_system_meta(tools) -}}
155
+ {{- '<|eot|>' -}}
156
+ {%- elif role == 'user' -%}
157
+ {{- '<|start|>user<|message|>' -}}
158
+ {{- render_content(message['content']) -}}
159
+ {{- '<|eot|>' -}}
160
+ {%- elif role == 'tool' -%}
161
+ {%- set tname = message.get('name') -%}
162
+ {%- if not tname -%}
163
+ {%- set tcid = message.get('tool_call_id') -%}
164
+ {%- set rns = namespace(name=tcid if tcid else '') -%}
165
+ {%- for m in messages -%}
166
+ {%- if m.get('tool_calls') -%}
167
+ {%- for tc in m['tool_calls'] -%}
168
+ {%- if tcid is not none and tc.id is defined and tc.id == tcid -%}
169
+ {%- set rns.name = tc.function.name -%}
170
+ {%- endif -%}
171
+ {%- endfor -%}
172
+ {%- endif -%}
173
+ {%- endfor -%}
174
+ {%- set tname = rns.name -%}
175
+ {%- endif -%}
176
+ {{- '<|start|>tool ' + tname + '<|message|><tool_output name="' + tname + '">\n' -}}
177
+ {{- render_content(message['content']) -}}
178
+ {{- '\n</tool_output><|eot|>' -}}
179
+ {%- elif role == 'assistant' -%}
180
+ {%- if message.get('reasoning_content') -%}
181
+ {{- '<|start|>assistant to=self<|message|>' + message['reasoning_content'] + '<|eom|>' -}}
182
+ {%- endif -%}
183
+ {%- if message.get('tool_calls') -%}
184
+ {%- for tc in message['tool_calls'] -%}
185
+ {{- '<|start|>assistant to=' + tc.function.name + '<|message|>' -}}
186
+ {{- render_atem(tc) -}}
187
+ {%- if loop.last -%}
188
+ {{- end_token -}}
189
+ {%- else -%}
190
+ {{- '<|eom|>' -}}
191
+ {%- endif -%}
192
+ {%- endfor -%}
193
+ {%- else -%}
194
+ {%- set recipient = message.get('recipient') or 'user' -%}
195
+ {%- set end_turn = message.get('end_turn') -%}
196
+ {%- if end_turn is none -%}
197
+ {%- set end_turn = not (recipient and recipient != 'user') -%}
198
+ {%- endif -%}
199
+ {{- '<|start|>assistant' -}}
200
+ {%- if recipient -%}
201
+ {{- ' to=' + recipient -}}
202
+ {%- endif -%}
203
+ {{- '<|message|>' -}}
204
+ {{- render_content(message['content']) -}}
205
+ {{- ('<|eot|>' if end_turn else '<|eom|>') -}}
206
+ {%- endif -%}
207
+ {%- endif -%}
208
+ {%- endfor -%}
209
+ {%- if add_generation_prompt -%}
210
+ {{- '<|start|>assistant' -}}
211
+ {%- endif -%}
config.json ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "MuseGlimmerForConditionalGeneration"
4
+ ],
5
+ "do_sample": true,
6
+ "dtype": "bfloat16",
7
+ "eos_token_id": [
8
+ 200001,
9
+ 200008
10
+ ],
11
+ "generation_config": {
12
+ "bos_token_id": 200000,
13
+ "eos_token_id": [
14
+ 200001,
15
+ 200008
16
+ ],
17
+ "pad_token_id": 200018,
18
+ "max_length": 131072,
19
+ "do_sample": true,
20
+ "temperature": 1.0,
21
+ "top_p": 0.95,
22
+ "top_k": 64
23
+ },
24
+ "image_token_id": 200092,
25
+ "model_type": "muse_glimmer",
26
+ "out_hidden_size": 6144,
27
+ "projector_hidden_act": "gelu",
28
+ "projector_hidden_size": 4096,
29
+ "quantization": {
30
+ "group_size": 64,
31
+ "bits": 4,
32
+ "mode": "affine"
33
+ },
34
+ "quantization_config": {
35
+ "group_size": 64,
36
+ "bits": 4,
37
+ "mode": "affine"
38
+ },
39
+ "temperature": 1.0,
40
+ "text_config": {
41
+ "attention_bias": false,
42
+ "attention_dropout": 0.0,
43
+ "bos_token_id": 200000,
44
+ "eos_token_id": 200001,
45
+ "final_logit_softcapping": 20.0,
46
+ "head_dim": 128,
47
+ "hidden_activation": "silu",
48
+ "hidden_size": 6656,
49
+ "initializer_range": 0.02,
50
+ "intermediate_size": 19968,
51
+ "layer_rope_theta": [
52
+ 500000.0,
53
+ 500000.0,
54
+ 500000.0,
55
+ 0,
56
+ 500000.0,
57
+ 500000.0,
58
+ 500000.0,
59
+ 0,
60
+ 500000.0,
61
+ 500000.0,
62
+ 500000.0,
63
+ 0,
64
+ 500000.0,
65
+ 500000.0,
66
+ 500000.0,
67
+ 0,
68
+ 500000.0,
69
+ 500000.0,
70
+ 500000.0,
71
+ 0,
72
+ 500000.0,
73
+ 500000.0,
74
+ 500000.0,
75
+ 0,
76
+ 500000.0,
77
+ 500000.0,
78
+ 500000.0,
79
+ 0,
80
+ 500000.0,
81
+ 500000.0,
82
+ 500000.0,
83
+ 0,
84
+ 500000.0,
85
+ 500000.0,
86
+ 500000.0,
87
+ 0,
88
+ 500000.0,
89
+ 500000.0,
90
+ 500000.0,
91
+ 0,
92
+ 500000.0,
93
+ 500000.0,
94
+ 500000.0,
95
+ 0,
96
+ 500000.0,
97
+ 500000.0,
98
+ 500000.0,
99
+ 0,
100
+ 500000.0,
101
+ 500000.0,
102
+ 500000.0,
103
+ 0
104
+ ],
105
+ "layer_types": [
106
+ "sliding_attention",
107
+ "sliding_attention",
108
+ "sliding_attention",
109
+ "full_attention",
110
+ "sliding_attention",
111
+ "sliding_attention",
112
+ "sliding_attention",
113
+ "full_attention",
114
+ "sliding_attention",
115
+ "sliding_attention",
116
+ "sliding_attention",
117
+ "full_attention",
118
+ "sliding_attention",
119
+ "sliding_attention",
120
+ "sliding_attention",
121
+ "full_attention",
122
+ "sliding_attention",
123
+ "sliding_attention",
124
+ "sliding_attention",
125
+ "full_attention",
126
+ "sliding_attention",
127
+ "sliding_attention",
128
+ "sliding_attention",
129
+ "full_attention",
130
+ "sliding_attention",
131
+ "sliding_attention",
132
+ "sliding_attention",
133
+ "full_attention",
134
+ "sliding_attention",
135
+ "sliding_attention",
136
+ "sliding_attention",
137
+ "full_attention",
138
+ "sliding_attention",
139
+ "sliding_attention",
140
+ "sliding_attention",
141
+ "full_attention",
142
+ "sliding_attention",
143
+ "sliding_attention",
144
+ "sliding_attention",
145
+ "full_attention",
146
+ "sliding_attention",
147
+ "sliding_attention",
148
+ "sliding_attention",
149
+ "full_attention",
150
+ "sliding_attention",
151
+ "sliding_attention",
152
+ "sliding_attention",
153
+ "full_attention",
154
+ "sliding_attention",
155
+ "sliding_attention",
156
+ "sliding_attention",
157
+ "full_attention"
158
+ ],
159
+ "max_position_embeddings": 131072,
160
+ "model_type": "muse_glimmer_text",
161
+ "num_attention_heads": 32,
162
+ "num_hidden_layers": 52,
163
+ "num_key_value_heads": 2,
164
+ "output_multiplier": 0.19611613513818404,
165
+ "pad_token_id": null,
166
+ "post_norm_eps": 1e-08,
167
+ "qk_scale_factor": 3.87,
168
+ "rms_norm_eps": 1e-05,
169
+ "rope_parameters": {
170
+ "rope_theta": 500000.0,
171
+ "rope_type": "default"
172
+ },
173
+ "sliding_window": 2048,
174
+ "tie_word_embeddings": false,
175
+ "use_cache": true,
176
+ "vocab_size": 202048
177
+ },
178
+ "top_k": 64,
179
+ "top_p": 0.95,
180
+ "transformers_version": "5.15.0.dev0",
181
+ "video_token_id": 200091,
182
+ "vision_config": {
183
+ "hidden_act": "gelu",
184
+ "hidden_size": 1536,
185
+ "intermediate_size": 8960,
186
+ "layer_norm_eps": 1e-05,
187
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2050
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2051
+ "<|reserved_special_token_2045|>",
2052
+ "<|reserved_special_token_2046|>",
2053
+ "<|reserved_special_token_2047|>"
2054
+ ],
2055
+ "is_local": true,
2056
+ "local_files_only": false,
2057
+ "model_max_length": 131072,
2058
+ "pad_token": "<|finetune_right_pad|>",
2059
+ "processor_class": "MuseGlimmerProcessor",
2060
+ "response_template": {
2061
+ "defaults": {
2062
+ "role": "assistant"
2063
+ },
2064
+ "fields": {
2065
+ "content": {
2066
+ "close": [
2067
+ "<|eot|>",
2068
+ "<|eom|>"
2069
+ ],
2070
+ "content": "text",
2071
+ "open_pattern": "to=user<\\|message\\|>"
2072
+ },
2073
+ "reasoning_content": {
2074
+ "close": "<|eom|>",
2075
+ "content": "text",
2076
+ "open_pattern": "to=self<\\|message\\|>"
2077
+ },
2078
+ "tool_calls": {
2079
+ "close": "</atem:invoke>",
2080
+ "content": "xml-inline",
2081
+ "content_args": {
2082
+ "tag_pattern": "<atem:parameter\\b[^>]*?\\bname=\"(?P<key>[^\"]+)\"[^>]*?>(?P<value>.*?)</atem:parameter>",
2083
+ "value_parser": {
2084
+ "args": {
2085
+ "allow_non_json": true
2086
+ },
2087
+ "name": "json"
2088
+ }
2089
+ },
2090
+ "open_pattern": "<atem:invoke\\b[^>]*?\\bname=\"(?P<name>[^\"]+)\">",
2091
+ "repeats": true,
2092
+ "transform": {
2093
+ "function": {
2094
+ "arguments": "{content}",
2095
+ "name": "{name}"
2096
+ },
2097
+ "type": "function"
2098
+ }
2099
+ }
2100
+ },
2101
+ "start_anchor": "<|start|>assistant"
2102
+ },
2103
+ "tokenizer_class": "TokenizersBackend"
2104
+ }