update

.gitattributes

@@ -33,3 +33,39 @@ 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
+qwen3_5_text_p512_l1_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l7_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l23_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l26_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l28_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l5_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l8_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_vision.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l10_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l20_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l0_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l21_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l22_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l24_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_post.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l15_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l18_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l31_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l14_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l16_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l27_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l3_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l6_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l11_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l13_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l25_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l29_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l2_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l9_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l12_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l17_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l4_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l19_together.axmodel filter=lfs diff=lfs merge=lfs -text
+qwen3_5_text_p512_l30_together.axmodel filter=lfs diff=lfs merge=lfs -text
+model.embed_tokens.weight.bfloat16.bin filter=lfs diff=lfs merge=lfs -text
+qwen3_5_tokenizer.txt filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,323 @@
 ---
 license: mit
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3.5-4B
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- Qwen3.5
+- Qwen3.5-4B
+- VLM
+- GPTQ
+- Int4
 ---
+
+# Qwen3.5-4B
+
+This version of Qwen3.5-4B has been converted to run on the Axera NPU using **w4a16** quantization. 
+
+Compatible with Pulsar2 version: 5.0
+
+## Convert tools links:
+
+For those who are interested in model conversion, you can try to export axmodel through the original repo : 
+
+- https://huggingface.co/Qwen/Qwen3.5-4B
+
+[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) 
+
+[AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/ax-llm.git) 
+
+
+## Support Platform
+
+- AX650
+  - AX650N DEMO Board
+  - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
+  - [M.2 Accelerator card](https://docs.m5stack.com/zh_CN/ai_hardware/LLM-8850_Card)
+
+**Image Process**
+|Chips| input size | image num  | ttft(168 tokens) | w4a16 | CMM | Flash |
+|--|--|--|--|--|--|--|
+|AX650| 384*384 | 1 | 845 ms | 5.7 tokens/sec| 4.91GiB | 5.59GiB |
+
+**Video Process**
+|Chips| input size | image num | ttft(600 tokens) | w4a16 | CMM | Flash |
+|--|--|--|--|--|--|--|
+|AX650| 384*384 | 8  |  2111 ms | 5.7 tokens/sec| 4.91GiB |  5.59GiB |
+
+
+
+The DDR capacity refers to the CMM memory that needs to be consumed. Ensure that the CMM memory allocation on the development board is greater than this value.
+
+## How to use
+
+## 安装 axllm
+方式一：克隆仓库后执行安装脚本：
+
+```shell
+git clone -b axllm https://github.com/AXERA-TECH/ax-llm.git
+cd ax-llm
+./install.sh
+```
+
+方式二：一行命令安装（默认分支 `axllm`）：
+
+```shell
+curl -fsSL https://raw.githubusercontent.com/AXERA-TECH/ax-llm/axllm/install.sh | bash
+```
+
+方式三：下载Github Actions CI 导出的可执行程序（适合没有编译环境的用户）：
+
+如果没有编译环境，请到：
+`https://github.com/AXERA-TECH/ax-llm/actions?query=branch%3Aaxllm`
+下载 **最新 CI 导出的可执行程序**（`axllm`），然后：
+
+```shell
+chmod +x axllm
+sudo mv axllm /usr/bin/axllm
+```
+
+## 模型下载（Hugging Face）
+先创建模型目录并进入，然后下载到该目录：
+
+```shell
+mkdir -p AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K
+cd AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K
+hf download AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K --local-dir .
+
+# structure of the downloaded files
+tree -L 3
+`-- AXERA-TECH
+    `-- Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K
+        |-- qwen3_5_vision.axmodel
+        |-- README.md
+        |-- config.json
+        |-- image.png
+        |-- model.embed_tokens.weight.bfloat16.bin
+        |-- post_config.json
+        |-- qwen3_5_tokenizer.txt
+        |-- qwen3_5_text_p128_l0_together.axmodel
+        ...
+        |-- qwen3_5_text_p128_l23_together.axmodel
+        |-- qwen3_5_text_post.axmodel
+        `-- vision_cache
+
+3 directories, 39 files
+```
+
+## Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board
+
+### 运行（CLI）
+
+```shell
+root@ax650 ~/yongqiang/lhj/Qwen3_5.AXERA/ax-llm # axllm run Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K/
+11:40:21.412 INF Init:218 | LLM init start
+11:40:21.412 INF Init:226 | mixed attention enabled: full_attention_interval=4 ref_full_layer_idx=3
+tokenizer_type = 3
+ 96% | ##############################   |  26 /  27 [3.78s<3.93s, 6.87 count/s] init post axmodel ok,remain_cmm(5589 MB)
+11:40:25.195 INF Init:368 | max_token_len : 2047
+11:40:25.195 INF Init:371 | kv_cache_size : 512, kv_cache_num: 2047
+11:40:25.195 INF Init:374 | prefill_token_num : 128
+11:40:25.195 INF Init:379 | grp: 1, prefill_max_kv_cache_num : 1
+11:40:25.195 INF Init:379 | grp: 2, prefill_max_kv_cache_num : 128
+11:40:25.195 INF Init:379 | grp: 3, prefill_max_kv_cache_num : 256
+11:40:25.195 INF Init:379 | grp: 4, prefill_max_kv_cache_num : 384
+11:40:25.195 INF Init:379 | grp: 5, prefill_max_kv_cache_num : 512
+11:40:25.195 INF Init:379 | grp: 6, prefill_max_kv_cache_num : 768
+11:40:25.195 INF Init:379 | grp: 7, prefill_max_kv_cache_num : 1024
+11:40:25.195 INF Init:379 | grp: 8, prefill_max_kv_cache_num : 1152
+11:40:25.195 INF Init:384 | prefill_max_token_num : 1152
+11:40:25.195 INF Init:27 | LLaMaEmbedSelector use mmap
+100% | ################################ |  27 /  27 [3.79s<3.79s, 7.13 count/s] embed_selector init ok
+11:40:25.478 INF Init:643 | Qwen-VL token ids: vision_start=248053 image_pad=248056 video_pad=248057
+11:40:25.478 INF Init:668 | VisionModule init ok: type=Qwen3VL, tokens_per_block=144, embed_size=2048, out_dtype=fp32
+11:40:25.478 WRN Init:677 | Vision preprocess backend: SimpleCV (OpenCV not found at build time; minor differences vs OpenCV are possible)
+11:40:25.480 INF load_config:282 | load config: 
+11:40:25.480 INF load_config:282 | {
+11:40:25.480 INF load_config:282 |     "enable_repetition_penalty": false,
+11:40:25.480 INF load_config:282 |     "enable_temperature": false,
+11:40:25.480 INF load_config:282 |     "enable_top_k_sampling": true,
+11:40:25.480 INF load_config:282 |     "enable_top_p_sampling": false,
+11:40:25.480 INF load_config:282 |     "penalty_window": 20,
+11:40:25.480 INF load_config:282 |     "repetition_penalty": 1.2,
+11:40:25.480 INF load_config:282 |     "temperature": 0.9,
+11:40:25.480 INF load_config:282 |     "top_k": 10,
+11:40:25.480 INF load_config:282 |     "top_p": 0.8
+11:40:25.480 INF load_config:282 | }
+11:40:25.480 INF Init:448 | LLM init ok
+Commands:
+  /q, /exit  退出
+  /reset     重置 kvcache
+  /dd        删除一轮对话
+  /pp        打印历史对话
+Ctrl+C: 停止当前生成
+VLM enabled: after each prompt, input image path (empty = text-only). Use "video:<frames_dir>" for video.
+----------------------------------------
+prompt >> 描述视频内容
+media >> video:assets/football.mp4:1.5
+17:54:19.424 INF extract_video_frames_ffmpeg:299 | Extracting raw video container to frames: assets/football.mp4 -> /tmp/axllm_video_frames/video_77305085315876_0 fps=1.4985014985014984
+17:54:40.848 INF collect_video_frame_paths:379 | Video fps sampling: path=assets/football.mp4 fps=1.5 duration=60.060s target_frames=90 selected=90
+17:55:01.670 INF SetKVCache:2543 | decode_grpid:3 prefill_grpid:6 history_cap:0 total_cap:256 symbolic_cap:1 precompute_len:0 input_num_token:6504 prefer_symbolic_group:0
+17:55:01.670 INF SetKVCache:2565 | current prefill_max_token_num:10496
+17:55:02.375 INF SetKVCache:2581 | first run
+17:55:02.481 INF Run:2738 | input token num : 6504, prefill_split_num : 26
+17:55:02.481 INF Run:2818 | prefill chunk p=0 history_len=0 grpid=6 kv_cache_num=0 input_tokens=256
+17:55:03.405 INF Run:2818 | prefill chunk p=1 history_len=256 grpid=8 kv_cache_num=512 input_tokens=256
+17:55:04.400 INF Run:2818 | prefill chunk p=2 history_len=512 grpid=9 kv_cache_num=768 input_tokens=256
+17:55:05.385 INF Run:2818 | prefill chunk p=3 history_len=768 grpid=10 kv_cache_num=1024 input_tokens=256
+17:55:06.362 INF Run:2818 | prefill chunk p=4 history_len=1024 grpid=11 kv_cache_num=1280 input_tokens=256
+17:55:07.365 INF Run:2818 | prefill chunk p=5 history_len=1280 grpid=12 kv_cache_num=1536 input_tokens=256
+17:55:08.399 INF Run:2818 | prefill chunk p=6 history_len=1536 grpid=13 kv_cache_num=1792 input_tokens=256
+17:55:09.466 INF Run:2818 | prefill chunk p=7 history_len=1792 grpid=14 kv_cache_num=2048 input_tokens=256
+17:55:10.592 INF Run:2818 | prefill chunk p=8 history_len=2048 grpid=15 kv_cache_num=2304 input_tokens=256
+17:55:11.649 INF Run:2818 | prefill chunk p=9 history_len=2304 grpid=16 kv_cache_num=2560 input_tokens=256
+17:55:12.724 INF Run:2818 | prefill chunk p=10 history_len=2560 grpid=17 kv_cache_num=2816 input_tokens=256
+17:55:13.798 INF Run:2818 | prefill chunk p=11 history_len=2816 grpid=18 kv_cache_num=3072 input_tokens=256
+17:55:14.886 INF Run:2818 | prefill chunk p=12 history_len=3072 grpid=19 kv_cache_num=3328 input_tokens=256
+17:55:15.985 INF Run:2818 | prefill chunk p=13 history_len=3328 grpid=20 kv_cache_num=3584 input_tokens=256
+17:55:17.091 INF Run:2818 | prefill chunk p=14 history_len=3584 grpid=21 kv_cache_num=3840 input_tokens=256
+17:55:18.232 INF Run:2818 | prefill chunk p=15 history_len=3840 grpid=22 kv_cache_num=4096 input_tokens=256
+17:55:19.386 INF Run:2818 | prefill chunk p=16 history_len=4096 grpid=23 kv_cache_num=4352 input_tokens=256
+17:55:20.613 INF Run:2818 | prefill chunk p=17 history_len=4352 grpid=24 kv_cache_num=4608 input_tokens=256
+17:55:21.853 INF Run:2818 | prefill chunk p=18 history_len=4608 grpid=25 kv_cache_num=4864 input_tokens=256
+17:55:23.114 INF Run:2818 | prefill chunk p=19 history_len=4864 grpid=26 kv_cache_num=5120 input_tokens=256
+17:55:24.395 INF Run:2818 | prefill chunk p=20 history_len=5120 grpid=27 kv_cache_num=5376 input_tokens=256
+17:55:25.681 INF Run:2818 | prefill chunk p=21 history_len=5376 grpid=28 kv_cache_num=5632 input_tokens=256
+17:55:26.995 INF Run:2818 | prefill chunk p=22 history_len=5632 grpid=29 kv_cache_num=5888 input_tokens=256
+17:55:28.322 INF Run:2818 | prefill chunk p=23 history_len=5888 grpid=30 kv_cache_num=6144 input_tokens=256
+17:55:29.667 INF Run:2818 | prefill chunk p=24 history_len=6144 grpid=31 kv_cache_num=6400 input_tokens=256
+17:55:31.043 INF Run:2818 | prefill chunk p=25 history_len=6400 grpid=32 kv_cache_num=6656 input_tokens=104
+17:55:32.459 INF Run:3045 | ttft: 29978.45 ms
+<think>17:55:32.459 INF Run:3076 | VLM decode positions: rope_start=6372 dense_kv_start=6504
+
+
+</think>
+
+这段视频是一个关于足球比赛的直播或录像，具体是2019年1月30日巴塞罗那队与塞维利亚队的比赛。视频展示了比赛中的精彩瞬间和进球。
+
+- **比赛场景**：视频展示了一场足球比赛的片段，场地为标准的足球场，有清晰的白色线条标记。
+- **球员和队服**：
+  - 巴塞罗那队穿着蓝色和红色条纹的球衣，号码为10的球员是路易斯·苏亚雷斯（Luis Suárez）。
+  - 塞维利亚队穿着白色球衣。
+- **比赛动作**：
+  - 在多个片段中，可以看到球员们在场上积极跑动和传球。
+  - 一个关键片段显示，巴塞罗那队在进攻中成功突破防守，最终由苏亚雷斯打入一球。
+  - 进球后，苏亚雷斯与队友庆祝，场面充满激情。
+- **观众和氛围**：
+  - 观众席上坐满了观众，他们热情地为比赛加油。
+  - 场边的广告牌上可以看到“Nike”和“Estrella Damm”等品牌标识。
+- **直播者**：
+  - 在视频的右上角，有一个直播者正在观看和评论比赛。他穿着巴塞罗那队的球衣，表情丰富，时而惊讶，时而兴奋，显然对比赛非常投入。
+- **技术细节**：
+  - 视频底部有“FBS00MPS10"的水印，以及播放进度条，显示视频正在播放中。
+  - 视频标题为“Barcelona vs Sevilla 6-1 All Goals & Extended Highlights 30/01/2019 HD"，表明这是该场比赛的完整进球和扩展集锦。
+
+这段视频通过多个镜头展示了比赛的紧张和精彩，以及球迷和直播者对比赛的热情。
+
+17:56:46.246 NTC Run:3445 | hit eos,decode avg 5.24 token/s
+17:56:46.246 INF GetKVCache:2496 | precompute_len:6892, remaining:3604 (tracked)
+```
+
+### 启动服务（OpenAI 兼容）
+
+```shell
+root@ax650:~# axllm serve AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K
+[I][                            Init][ 138]: LLM init start
+tokenizer_type = 1
+ 96% | ███████████████████████████████   |  30 /  31 [4.63s<4.79s, 6.47 count/s] init post axmodel ok,remain_cmm(9563 MB)
+[I][                            Init][ 199]: max_token_len : 2047
+[I][                            Init][ 202]: kv_cache_size : 1024, kv_cache_num: 2047
+[I][                            Init][ 205]: prefill_token_num : 128
+[I][                            Init][ 209]: grp: 1, prefill_max_kv_cache_num : 1
+[I][                            Init][ 209]: grp: 2, prefill_max_kv_cache_num : 128
+[I][                            Init][ 209]: grp: 3, prefill_max_kv_cache_num : 256
+[I][                            Init][ 209]: grp: 4, prefill_max_kv_cache_num : 384
+[I][                            Init][ 209]: grp: 5, prefill_max_kv_cache_num : 512
+[I][                            Init][ 209]: grp: 6, prefill_max_kv_cache_num : 640
+[I][                            Init][ 209]: grp: 7, prefill_max_kv_cache_num : 768
+[I][                            Init][ 209]: grp: 8, prefill_max_kv_cache_num : 896
+[I][                            Init][ 209]: grp: 9, prefill_max_kv_cache_num : 1024
+[I][                            Init][ 209]: grp: 10, prefill_max_kv_cache_num : 1152
+[I][                            Init][ 214]: prefill_max_token_num : 1152
+[I][                            Init][  27]: LLaMaEmbedSelector use mmap
+100% | ████████████████████████████████ |  31 /  31 [4.64s<4.64s, 6.69 count/s] embed_selector init ok
+[W][                            Init][ 457]: Qwen-VL vision size override: cfg=448x448 bytes=1204224, model_input_bytes=884736 -> 384x384 (square).
+[I][                            Init][ 641]: Qwen-VL token ids: vision_start=151652 image_pad=151655 video_pad=151656
+[I][                            Init][ 666]: VisionModule init ok: type=Qwen3VL, tokens_per_block=144, embed_size=2048, out_dtype=fp32
+[I][                            Init][ 672]: VisionModule deepstack enabled: layers=3
+[I][                     load_config][ 282]: load config:
+{
+    "enable_repetition_penalty": false,
+    "enable_temperature": false,
+    "enable_top_k_sampling": false,
+    "enable_top_p_sampling": false,
+    "penalty_window": 20,
+    "repetition_penalty": 1.2,
+    "temperature": 0.9,
+    "top_k": 10,
+    "top_p": 0.8
+}
+
+[I][                            Init][ 272]: LLM init ok
+Starting server on port 8000 with model 'AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K'...
+OpenAI API Server starting on http://0.0.0.0:8000
+Max concurrency: 1
+Models: AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K
+```
+
+### OpenAI 调用示例
+
+```python
+from openai import OpenAI
+
+API_URL = "http://127.0.0.1:8000/v1"
+MODEL = "AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K"
+
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "you are a helpful assistant."}]},
+    {"role": "user", "content": "hello"},
+]
+
+client = OpenAI(api_key="not-needed", base_url=API_URL)
+completion = client.chat.completions.create(
+    model=MODEL,
+    messages=messages,
+)
+
+print(completion.choices[0].message.content)
+```
+
+
+### OpenAI 流式调用示例
+
+```python
+from openai import OpenAI
+
+API_URL = "http://127.0.0.1:8000/v1"
+MODEL = "AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C256-P10K-CTX12K"
+
+messages = [
+    {"role": "system", "content": [{"type": "text", "text": "you are a helpful assistant."}]},
+    {"role": "user", "content": "hello"},
+]
+
+client = OpenAI(api_key="not-needed", base_url=API_URL)
+stream = client.chat.completions.create(
+    model=MODEL,
+    messages=messages,
+    stream=True,
+)
+
+print("assistant:")
+for ev in stream:
+    delta = getattr(ev.choices[0], "delta", None)
+    if delta and getattr(delta, "content", None):
+        print(delta.content, end="", flush=True)
+print("
+")
+```
+

config.json

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+{
+  "system_prompt": "you are a helpful assistant.",
+  "model_name": "AXERA-TECH/Qwen3.5-4B-AX650-GPTQ-Int4-C512-P30k-CTX32k",
+  "url_tokenizer_model": "qwen3_5_tokenizer.txt",
+  "tokenizer_type": "Qwen3_5VL",
+  "post_config_path": "post_config.json",
+  "template_filename_axmodel": "qwen3_5_text_p512_l%d_together.axmodel",
+  "axmodel_num": 32,
+  "full_attention_interval": 4,
+  "filename_post_axmodel": "qwen3_5_text_post.axmodel",
+  "filename_tokens_embed": "model.embed_tokens.weight.bfloat16.bin",
+  "tokens_embed_num": 248320,
+  "tokens_embed_size": 2560,
+  "b_use_mmap_load_embed": true,
+  "b_use_mmap_load_layer": true,
+  "vlm_type": "Qwen3VL",
+  "filename_image_encoder_axmodel": "qwen3_5_vision.axmodel",
+  "vision_patch_size": 16,
+  "vision_width": 384,
+  "vision_height": 384,
+  "vision_temporal_patch_size": 2,
+  "vision_spatial_merge_size": 2,
+  "vision_fps": 1,
+  "vision_tokens_per_second": 1,
+  "vision_cache_dir": "vision_cache",
+  "devices": [
+    0
+  ]
+}

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+{
+    "enable_temperature" : false,
+    "temperature" : 0.7,
+    
+    "enable_repetition_penalty" : false,
+    "repetition_penalty" : 1.0,
+    "penalty_window" : 20,
+
+    "enable_top_p_sampling" : false,
+    "top_p" : 0.8,
+
+    "enable_top_k_sampling" : false,
+    "top_k" : 20
+}

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