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@@ -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

README.md

@@ -0,0 +1,126 @@
+---
+language:
+- en
+- zh
+license: apache-2.0
+base_model: Jackrong/Qwopus3.5-27B-v3
+tags:
+- qwen3.5
+- reasoning
+- quantized
+- fp8
+- fp8-dynamic
+- compressed-tensors
+- deltanet
+- chain-of-thought
+- mtp
+pipeline_tag: text-generation
+library_name: transformers
+model_name: Qwopus3.5-27B-v3-FP8-Dynamic
+quantized_by: mconcat
+---
+
+# Qwopus3.5-27B-v3-FP8-Dynamic
+
+FP8 Dynamic quantized version of [Jackrong/Qwopus3.5-27B-v3](https://huggingface.co/Jackrong/Qwopus3.5-27B-v3).
+
+This checkpoint preserves the hybrid Qwen3.5 DeltaNet + softmax architecture and MTP (Multi-Token Prediction) head from the BF16 source, quantizing most linear layers to FP8 W8A8 while keeping the most sensitive projections and sidecar components in BF16.
+
+## Verified Inference
+
+Local export and sanity-check evaluation were verified on **2026-04-07** on a single **NVIDIA RTX PRO 6000 Blackwell Workstation Edition (96 GB)** with:
+
+- `transformers==5.3.0`
+- `llm-compressor==0.14.1.dev24`
+- `vllm==0.17.1`
+
+What was verified:
+
+- FP8 export completed successfully via llm-compressor
+- MTP weights are included in the main safetensors file
+- The checkpoint loads in vLLM and generates correct output
+- Quick perplexity sanity check: **7.67** (FineWeb-Edu, 50 samples)
+
+## Quantization Strategy
+
+Uniform FP8_DYNAMIC quantization using [llm-compressor](https://github.com/vllm-project/llm-compressor):
+
+| Precision | Layers |
+|-----------|--------|
+| **FP8 W8A8** | most `Linear` layers (per-channel static weight scales, per-token dynamic input scales) |
+| **BF16** | `lm_head`, `embed_tokens`, `self_attn.o_proj`, DeltaNet `linear_attn.out_proj`, DeltaNet `in_proj_a`/`in_proj_b`, visual encoder, MTP sidecar |
+
+Architecture match with the BF16 source:
+
+- `model_type=qwen3_5`
+- `64` text layers (hybrid DeltaNet + softmax, `full_attention_interval=4`)
+- `mtp_num_hidden_layers=1`
+- `max_position_embeddings=262144`
+- `hidden_size=5120`, `intermediate_size=17408`
+- `vocab_size=248320`
+
+## Usage
+
+### vLLM
+
+```bash
+pip install -U vllm>=0.17.0 transformers>=5.3.0
+```
+
+Standard serving:
+
+```bash
+vllm serve mconcat/Qwopus3.5-27B-v3-FP8-Dynamic \
+  --max-model-len 32768 \
+  --gpu-memory-utilization 0.85 \
+  --max-num-seqs 1 \
+  --skip-mm-profiling \
+  --reasoning-parser qwen3
+```
+
+With MTP speculative decoding:
+
+```bash
+vllm serve mconcat/Qwopus3.5-27B-v3-FP8-Dynamic \
+  --max-model-len 32768 \
+  --gpu-memory-utilization 0.85 \
+  --max-num-seqs 1 \
+  --skip-mm-profiling \
+  --reasoning-parser qwen3 \
+  --speculative-config '{"method":"mtp","num_speculative_tokens":1}'
+```
+
+### Transformers
+
+```python
+from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration
+import torch
+
+model = Qwen3_5ForConditionalGeneration.from_pretrained(
+    "mconcat/Qwopus3.5-27B-v3-FP8-Dynamic",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "mconcat/Qwopus3.5-27B-v3-FP8-Dynamic",
+    trust_remote_code=True,
+)
+```
+
+## Compatibility
+
+| Framework | Supported | Notes |
+|-----------|-----------|-------|
+| vLLM >= 0.17.0 | Yes | Verified with `vllm==0.17.1` on Blackwell; MTP works |
+| transformers >= 5.3.0 | Yes | Direct loading with `device_map="auto"` |
+| SGLang | Unknown | Not verified |
+
+## Notes
+
+- This export keeps `self_attn.o_proj` and DeltaNet `linear_attn.out_proj` in BF16 to preserve output projection fidelity.
+- MTP weights are embedded in the main `model.safetensors` file (no separate `model.mtp.safetensors`).
+- The model includes a vision encoder (loaded but unused for text-only inference). Use `--skip-mm-profiling` with vLLM to skip vision encoder profiling.
+- **Blackwell (SM120) note:** If you encounter TMA-related crashes, apply the one-line vLLM patch to disable TMA on Blackwell: change `>= 9` to `9 <= x < 12` in `vllm/model_executor/layers/fla/ops/utils.py`.
+- **KV cache:** Do not use `--kv-cache-dtype fp8_e4m3` with this model family — the checkpoint lacks calibrated KV scales and will produce degraded output. Use the default BF16 KV cache.

chat_template.jinja

@@ -0,0 +1,88 @@
+{%- 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 %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- 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" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- 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
+<think>
+' }}
+{%- endif %}

config.json

@@ -0,0 +1,467 @@
+{
+  "architectures": [
+    "Qwen3_5ForConditionalGeneration"
+  ],
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248046,
+  "image_token_id": 248056,
+  "model_name": "unsloth/Qwen3.5-27B",
+  "model_type": "qwen3_5",
+  "pad_token_id": 248055,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "format": "float-quantized",
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "token",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "memoryless_minmax",
+          "observer_kwargs": {},
+          "scale_dtype": null,
+          "strategy": "channel",
+          "symmetric": true,
+          "type": "float",
+          "zp_dtype": null
+        }
+      }
+    },
+    "format": "float-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
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+      "model.visual.blocks.0.attn.proj",
+      "model.visual.blocks.0.mlp.linear_fc1",
+      "model.visual.blocks.0.mlp.linear_fc2",
+      "model.visual.blocks.1.attn.qkv",
+      "model.visual.blocks.1.attn.proj",
+      "model.visual.blocks.1.mlp.linear_fc1",
+      "model.visual.blocks.1.mlp.linear_fc2",
+      "model.visual.blocks.2.attn.qkv",
+      "model.visual.blocks.2.attn.proj",
+      "model.visual.blocks.2.mlp.linear_fc1",
+      "model.visual.blocks.2.mlp.linear_fc2",
+      "model.visual.blocks.3.attn.qkv",
+      "model.visual.blocks.3.attn.proj",
+      "model.visual.blocks.3.mlp.linear_fc1",
+      "model.visual.blocks.3.mlp.linear_fc2",
+      "model.visual.blocks.4.attn.qkv",
+      "model.visual.blocks.4.attn.proj",
+      "model.visual.blocks.4.mlp.linear_fc1",
+      "model.visual.blocks.4.mlp.linear_fc2",
+      "model.visual.blocks.5.attn.qkv",
+      "model.visual.blocks.5.attn.proj",
+      "model.visual.blocks.5.mlp.linear_fc1",
+      "model.visual.blocks.5.mlp.linear_fc2",
+      "model.visual.blocks.6.attn.qkv",
+      "model.visual.blocks.6.attn.proj",
+      "model.visual.blocks.6.mlp.linear_fc1",
+      "model.visual.blocks.6.mlp.linear_fc2",
+      "model.visual.blocks.7.attn.qkv",
+      "model.visual.blocks.7.attn.proj",
+      "model.visual.blocks.7.mlp.linear_fc1",
+      "model.visual.blocks.7.mlp.linear_fc2",
+      "model.visual.blocks.8.attn.qkv",
+      "model.visual.blocks.8.attn.proj",
+      "model.visual.blocks.8.mlp.linear_fc1",
+      "model.visual.blocks.8.mlp.linear_fc2",
+      "model.visual.blocks.9.attn.qkv",
+      "model.visual.blocks.9.attn.proj",
+      "model.visual.blocks.9.mlp.linear_fc1",
+      "model.visual.blocks.9.mlp.linear_fc2",
+      "model.visual.blocks.10.attn.qkv",
+      "model.visual.blocks.10.attn.proj",
+      "model.visual.blocks.10.mlp.linear_fc1",
+      "model.visual.blocks.10.mlp.linear_fc2",
+      "model.visual.blocks.11.attn.qkv",
+      "model.visual.blocks.11.attn.proj",
+      "model.visual.blocks.11.mlp.linear_fc1",
+      "model.visual.blocks.11.mlp.linear_fc2",
+      "model.visual.blocks.12.attn.qkv",
+      "model.visual.blocks.12.attn.proj",
+      "model.visual.blocks.12.mlp.linear_fc1",
+      "model.visual.blocks.12.mlp.linear_fc2",
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+      "model.visual.blocks.13.mlp.linear_fc1",
+      "model.visual.blocks.13.mlp.linear_fc2",
+      "model.visual.blocks.14.attn.qkv",
+      "model.visual.blocks.14.attn.proj",
+      "model.visual.blocks.14.mlp.linear_fc1",
+      "model.visual.blocks.14.mlp.linear_fc2",
+      "model.visual.blocks.15.attn.qkv",
+      "model.visual.blocks.15.attn.proj",
+      "model.visual.blocks.15.mlp.linear_fc1",
+      "model.visual.blocks.15.mlp.linear_fc2",
+      "model.visual.blocks.16.attn.qkv",
+      "model.visual.blocks.16.attn.proj",
+      "model.visual.blocks.16.mlp.linear_fc1",
+      "model.visual.blocks.16.mlp.linear_fc2",
+      "model.visual.blocks.17.attn.qkv",
+      "model.visual.blocks.17.attn.proj",
+      "model.visual.blocks.17.mlp.linear_fc1",
+      "model.visual.blocks.17.mlp.linear_fc2",
+      "model.visual.blocks.18.attn.qkv",
+      "model.visual.blocks.18.attn.proj",
+      "model.visual.blocks.18.mlp.linear_fc1",
+      "model.visual.blocks.18.mlp.linear_fc2",
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+      "model.visual.blocks.19.mlp.linear_fc1",
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+      "model.visual.blocks.20.attn.proj",
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+      "model.visual.blocks.26.mlp.linear_fc2",
+      "model.visual.merger.linear_fc1",
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