Instructions to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000")
model = AutoModelForCausalLM.from_pretrained("ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000

SGLang

How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000 with Docker Model Runner:
```
docker model run hf.co/ccui46/hazardworld_per_chunk_act_glm_tokfix_diffPrompt_3000
```

ccui46 commited on Apr 14

Commit

cce51b2

verified ·

1 Parent(s): 9b9b025

Tokenizer for checkpoint at step 3000

Browse files

Files changed (4) hide show

.gitattributes +1 -0
chat_template.jinja +45 -0
tokenizer.json +3 -0
tokenizer_config.json +16 -0

.gitattributes CHANGED Viewed

@@ -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

 *.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

chat_template.jinja ADDED Viewed

	@@ -0,0 +1,45 @@

+[gMASK]<sop>
+{%- if tools -%}
+<|system|>
+你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的，你的任务是针对用户的问题和要求提供适当的答复和支持。
+# 可用工具
+{%- for tool in tools %}
+    {%- set function = tool.function if tool.get("function") else tool %}
+## {{ function.name }}
+{{ function | tojson(indent=4, ensure_ascii=False) }}
+在调用上述函数时，请使用 Json 格式表示调用的参数。
+{%- endfor %}
+{%- endif -%}
+{%- for msg in messages %}
+    {%- if msg.role == 'system' %}
+<|system|>
+{{ msg.content }}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages if message.role != 'system' %}
+    {%- set role = message['role'] %}
+    {%- set content = message['content'] %}
+    {%- set visible = content.split('</think>')[-1].strip() %}
+    {%- set meta = message.get("metadata", "") %}
+    {%- if role == 'user' %}
+<|user|>
+{{ visible }}
+    {%- elif role == 'assistant' and not meta %}
+<|assistant|>
+{{ visible }}
+    {%- elif role == 'assistant' and meta %}
+<|assistant|>{{ meta }}
+{{ visible }}
+    {%- elif role == 'observation' %}
+<|observation|>
+{{ visible }}
+    {%- endif %}
+{%- endfor %}
+{% if add_generation_prompt %}<|assistant|>
+<think>{% endif %}

tokenizer.json ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:76ebeac0d8bd7879ead7b43c16b44981f277e47225de2bd7de9ae1a6cc664a8c
+size 19966496

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,16 @@

+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "<|endoftext|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 128000,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "remove_space": false,
+  "tokenizer_class": "TokenizersBackend"
+}