Fix longwriter docs

docs/en/third_party/longwriter.md

@@ -4,14 +4,18 @@
 The LongWriter supports 10,000+ Word Generation From Long Context LLMs.
 We can use the benchmark **LongBench-Write** focuses more on measuring the long output quality as well as the output length.
 
-Refer to [LongWriter](https://github.com/THUDM/LongWriter)
+GitHub: [LongWriter](https://github.com/THUDM/LongWriter)
+
+Technical Report: [Minimum Tuning to Unlock Long Output from LLMs with High Quality Data as the Key](https://arxiv.org/abs/2410.10210)
+
 
 ## Usage
 
 ### Installation
 
 ```bash
-pip install evalscope[framework]
+pip install evalscope[framework] -U
+pip install vllm -U
 ```
 
 ### Task configuration
@@ -25,52 +29,78 @@ task_cfg = dict(stage=['infer', 'eval_l', 'eval_q'],
                 model='ZhipuAI/LongWriter-glm4-9b',
                 input_data_path=None,
                 output_dir='./outputs',
-                openai_api_key=None,
-                openai_gpt_model='gpt-4o-2024-05-13',
-                infer_generation_kwargs={
-                    'max_new_tokens': 32768,
-                    'temperature': 0.5
+                infer_config={
+                    'openai_api_base': 'http://127.0.0.1:8000/v1/chat/completions', 
+                    'is_chat': True, 
+                    'verbose': False, 
+                    'generation_kwargs': {
+                        'max_new_tokens': 32768, 
+                        'temperature': 0.5, 
+                        'repetition_penalty': 1.0
+                    },
+                    'proc_num': 16,
                 },
-                eval_generation_kwargs={
-                    'max_new_tokens': 1024,
-                    'temperature': 0.5,
-                    'stop': None
-                },
-                proc_num=8)
+                eval_config={
+                    # No need to set OpenAI info if skipping the stage `eval_q`
+                    'openai_api_key': None,   
+                    'openai_api_base': 'https://api.openai.com/v1/chat/completions', 
+                    'openai_gpt_model': 'gpt-4o-2024-05-13', 
+                    'generation_kwargs': {
+                        'max_new_tokens': 1024, 
+                        'temperature': 0.5, 
+                        'stop': None
+                    }, 
+                    'proc_num': 8
+                }
+            )
 
 ```
+
 - Arguments:
-  - `stage`: To run multiple stages, `infer`--run the inference process. `eval_l`--run eval length process. `eval_q`--run eval quality process.
-  - `model`: model id on the ModelScope hub, or local model dir.
+  - `stage`: To run multiple stages, `infer`--run the inference process. `eval_l`--run eval length process. `eval_q`--run eval quality process with the model-as-judge.
+  - `model`: model id on the ModelScope hub, or local model dir. Refer to [LongWriter-glm4-9b](https://modelscope.cn/models/ZhipuAI/LongWriter-glm4-9b/summary) for more details.
   - `input_data_path`: input data path, default to `None`, it means to use [longbench_write](https://github.com/modelscope/evalscope/blob/main/evalscope/third_party/longbench_write/resources/longbench_write.jsonl)
   - `output_dir`: output root directory.
   - `openai_api_key`: openai_api_key when enabling the stage `eval_q` to use `Model-as-Judge`. Default to None if not needed.
   - `openai_gpt_model`: Judge model name from OpenAI. Default to `gpt-4o-2024-05-13`
-  - `infer_generation_kwargs`: The generation kwargs for models to be evaluated.
-  - `eval_generation_kwargs`: The generation kwargs for judge-models.
-  - `proc_num`: proc num.
+  - `generation_kwargs`: The generation configs.
+  - `proc_num`: process number for inference and evaluation.
 
 
 2. Configuration with json (Optional):
 
 ```json
 {
-    "stage": ["infer", "eval_l", "eval_q"],
+    "stage": [
+        "infer",
+        "eval_l",
+        "eval_q"
+    ],
     "model": "ZhipuAI/LongWriter-glm4-9b",
     "input_data_path": null,
     "output_dir": "./outputs",
-    "openai_api_key": null,
-    "openai_gpt_model": "gpt-4o-2024-05-13",
-    "infer_generation_kwargs": {
-        "max_new_tokens": 32768,
-        "temperature": 0.5
+    "infer_config": {
+        "openai_api_base": "http://127.0.0.1:8000/v1/chat/completions",
+        "is_chat": true,
+        "verbose": false,
+        "generation_kwargs": {
+            "max_new_tokens": 32768,
+            "temperature": 0.5,
+            "repetition_penalty": 1.0
+        },
+        "proc_num": 16
     },
-    "eval_generation_kwargs": {
-        "max_new_tokens": 1024,
-        "temperature": 0.5,
-        "stop": null
-    },
-    "proc_num": 8
+    "eval_config": {
+        "openai_api_key": null,
+        "openai_api_base": "https://api.openai.com/v1/chat/completions",
+        "openai_gpt_model": "gpt-4o-2024-05-13",
+        "generation_kwargs": {
+            "max_new_tokens": 1024,
+            "temperature": 0.5,
+            "stop": null
+        },
+        "proc_num": 8
+    }
 }
 ```
 Refer to [default_task.json](https://github.com/modelscope/evalscope/blob/main/evalscope/third_party/longbench_write/default_task.json) for more details.
@@ -83,26 +113,54 @@ stage:
   - infer
   - eval_l
   - eval_q
-model: ZhipuAI/LongWriter-glm4-9b
+model: "ZhipuAI/LongWriter-glm4-9b"
 input_data_path: null
-output_dir: ./outputs
-openai_api_key: null
-openai_gpt_model: gpt-4o-2024-05-13
-infer_generation_kwargs:
-  max_new_tokens: 32768
-  temperature: 0.5
-eval_generation_kwargs:
-  max_new_tokens: 1024
-  temperature: 0.5
-  stop: null
-proc_num: 8
+output_dir: "./outputs"
+infer_config:
+  openai_api_base: "http://127.0.0.1:8000/v1/chat/completions"
+  is_chat: true
+  verbose: false
+  generation_kwargs:
+    max_new_tokens: 32768
+    temperature: 0.5
+    repetition_penalty: 1.0
+  proc_num: 16
+eval_config:
+  openai_api_key: null
+  openai_api_base: "https://api.openai.com/v1/chat/completions"
+  openai_gpt_model: "gpt-4o-2024-05-13"
+  generation_kwargs:
+    max_new_tokens: 1024
+    temperature: 0.5
+    stop: null
+  proc_num: 8
 
 ```
 Refer to [default_task.yaml](https://github.com/modelscope/evalscope/blob/main/evalscope/third_party/longbench_write/default_task.yaml) for more details.
 
 
+### Run Model Inference
+We recommend to use the [vLLM](https://github.com/vllm-project/vllm) to deploy the model.
+
+Environment:
+* A100(80G) x 1
+
+
+To start vLLM server, run the following command:
+```shell
+CUDA_VISIBLE_DEVICES=0 VLLM_USE_MODELSCOPE=True vllm serve --max-model-len=65536 --gpu_memory_utilization=0.95 --trust-remote-code ZhipuAI/LongWriter-glm4-9b
+
+```
+- Arguments:
+  - `max-model-len`: The maximum length of the model input.
+  - `gpu_memory_utilization`: The GPU memory utilization.
+  - `trust-remote-code`: Whether to trust the remote code.
+  - `model`: Could be a model id on the ModelScope/HuggingFace hub, or a local model dir.
+
+* Note: You can use multiple GPUs by setting `CUDA_VISIBLE_DEVICES=0,1,2,3` alternatively.
+
 
-### Run the task
+### Run Evaluation
 
 ```python
 from evalscope.third_party.longbench_write import run_task