Publish experimental BF16 LiveMem MLX checkpoint

Preserve weight bytes; include public provenance, upstream license, notices and standalone-runtime instructions. Quantized artifacts are experimental and not quality-certified.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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NOTICE

@@ -0,0 +1,13 @@
+LiveMem-RL MLX BF16
+
+Original checkpoint: chen-l/LiveMem-RL
+Original revision: 3402dcace8766f9f4e0c8f80928c1795cc073f39
+Base-model lineage: Qwen/Qwen3-4B-Instruct-2507
+Upstream license: Apache-2.0; Copyright 2024 Alibaba Cloud (preserved in LICENSE).
+
+Original model weights and tokenizer assets are unchanged. This is a BF16 mirror.
+Publication removed Transformers auto_map configuration and added public provenance, documentation and notices. No remote Python code is included.
+
+LiveMem paper: Zhichen Liu, Ruihan Sun, Hengjie Yang, Zipeng Wu, Zhaohan Chen, Xiaofan Zhang, Yang Xu (2026). https://arxiv.org/abs/2608.02515
+Runtime: https://github.com/popbones/livemem-mlx/releases/tag/v0.1.0
+This is an independent publication, not an official upstream release or endorsement.

README.md

@@ -0,0 +1,73 @@
+---
+{
+  "license": "apache-2.0",
+  "library_name": "livemem-mlx",
+  "pipeline_tag": "text-generation",
+  "base_model": "chen-l/LiveMem-RL",
+  "tags": [
+    "mlx",
+    "livemem",
+    "qwen3",
+    "experimental"
+  ],
+  "inference": false
+}
+---
+
+# LiveMem-RL MLX — BF16
+
+This is an **unchanged-weight BF16 mirror** of the official `chen-l/LiveMem-RL` checkpoint at the pinned revision below, packaged for use with the standalone MLX runtime. No training or quantization was performed. The original authors provide the model; this repository does not claim authorship of it.
+
+Experimental inference packaging. This mirror does not establish new model capabilities or improve the upstream checkpoint.
+
+## Source and identity
+
+- Original model: [chen-l/LiveMem-RL](https://huggingface.co/chen-l/LiveMem-RL).
+- Pinned original revision: `3402dcace8766f9f4e0c8f80928c1795cc073f39`.
+- Original BF16 weight SHA-256: `1a431577034b0ce43535d20fde78df765a19b992ad0130cdd7826e63fe5f13f0`.
+- Base-model lineage: [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507).
+- Runtime: [LiveMem MLX v0.1.0](https://github.com/popbones/livemem-mlx/releases/tag/v0.1.0).
+- This repository weight SHA-256: `1a431577034b0ce43535d20fde78df765a19b992ad0130cdd7826e63fe5f13f0`.
+
+## Precision
+
+All original checkpoint weights remain BF16. The runtime uses FP32 recurrent matrices. Attention KV and convolution state are separate.
+
+The underlying model has approximately 6.35 billion parameters, including the memory branch. The weight file is 12.70 GB. Weight storage is not total runtime memory.
+
+## Run locally on Apple Silicon
+
+Use Python 3.11 or later and an Apple Silicon Mac with Metal support.
+
+```sh
+python -m pip install "git+https://github.com/popbones/livemem-mlx.git@v0.1.0"
+livemem-mlx generate --model Popbones/LiveMem-RL-mlx-bf16 --prompt "Hello" --max-tokens 64
+```
+
+```python
+from livemem_mlx import load, generate
+
+loaded = load("Popbones/LiveMem-RL-mlx-bf16")
+result = generate(loaded, [{"role": "user", "content": "Hello"}], max_tokens=64)
+print(result.text)
+```
+
+After download, pin the repository commit with `--revision` or `load(..., revision=...)` for reproducibility. Default loading follows this repository main branch. Local paths, offline loading and `--cache-dir` are also supported.
+
+These files require `livemem_mlx.load`; this custom architecture is not registered as a built-in Transformers AutoModel or mlx-lm model. No remote Python implementation is included or executed. Hub hosting does not provide a hosted inference endpoint.
+
+## Validation and limits
+
+The corresponding checkpoint passed four standalone compatibility cases against its pre-extraction MLX runtime: Aurora, Zephyr, fresh-session Aurora replay, and a 2,283-token prompt with physical KV eviction followed by a simple arithmetic query. Generated token IDs, retained KV positions and saved prefill logits matched exactly within this precision variant.
+
+Peak MLX allocation was 12.41 GiB on an Apple M1 Pro / 32 GiB for that 2,048-token KV-budget test. It is not an 8K/32K serving benchmark, total system RAM guarantee or broad quality evaluation. Full-model CUDA/vLLM parity, general long-term-memory quality and long-running stability are not established.
+
+See the [compatibility report](https://github.com/popbones/livemem-mlx/blob/v0.1.0/docs/checkpoint-validation.md). This publication does not add a new benchmark result. Each fresh generation starts with separate state; state is not automatically retained across independent requests.
+
+## License and attribution
+
+The model distribution retains the upstream Apache-2.0 license. See LICENSE and NOTICE. The weights are unchanged. Source, precision settings, validated runtime and file hashes are recorded in manifest.json. Transformer auto_map metadata was removed to avoid advertising absent remote Python files. The original model and tokenizer assets remain credited to their authors.
+
+LiveMem: *Maintaining Memory State Continuity in Long-Running LLM Inference* — Zhichen Liu, Ruihan Sun, Hengjie Yang, Zipeng Wu, Zhaohan Chen, Xiaofan Zhang and Yang Xu (2026), [arXiv:2608.02515](https://arxiv.org/abs/2608.02515).
+
+This independent mirror/conversion is not endorsed by the LiveMem authors, Qwen or Alibaba.

chat_template.jinja

@@ -0,0 +1,61 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,81 @@
+{
+  "architectures": [
+    "LiveMemForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "mem_conv_bias": false,
+  "mem_conv_size": 4,
+  "mem_expand_v": 1.0,
+  "mem_head_dim": 128,
+  "mem_layers": null,
+  "mem_norm_eps": 1e-06,
+  "mem_num_heads": 32,
+  "mem_num_v_heads": 32,
+  "mem_o_proj_zero_init": true,
+  "memory_design": "X",
+  "model_type": "livemem",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "transformers_version": "5.12.1",
+  "use_cache": true
+}

manifest.json

@@ -0,0 +1,56 @@
+{
+  "format": "livemem-mlx-publication-v1",
+  "status": "complete",
+  "precision": "bfloat16",
+  "provenance": {
+    "source_model": "chen-l/LiveMem-RL",
+    "source_revision": "3402dcace8766f9f4e0c8f80928c1795cc073f39",
+    "source_weight_sha256": "1a431577034b0ce43535d20fde78df765a19b992ad0130cdd7826e63fe5f13f0",
+    "artifact_kind": "unchanged-weight BF16 mirror",
+    "runtime_repository": "https://github.com/popbones/livemem-mlx",
+    "validated_runtime_release": "v0.1.0",
+    "validated_runtime_commit": "0e38b4c0bda3234c0ce56977bd1a77a1db265657",
+    "config_changes": [
+      "Removed Transformers auto_map: use the standalone MLX runtime; remote Python is not included"
+    ],
+    "weights_changed_by_publication": false
+  },
+  "files": {
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+    },
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+      "sha256": "be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506"
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+      "sha256": "812e596fcd1e5d70809e670df067ec368a331875b3dad470c09e9b87121ae53f"
+    }
+  }
+}

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}