Text Generation
Transformers
Safetensors
glm4
cooking
recipes
dpo
provenance
abstention
conversational
Instructions to use HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant") model = AutoModelForCausalLM.from_pretrained("HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant
- SGLang
How to use HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant 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 "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant" \ --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": "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant", "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 "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant" \ --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": "HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant with Docker Model Runner:
docker model run hf.co/HumanjavaEnterprises/GLM-4-9B-Cooking-Assistant
File size: 4,170 Bytes
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"added_tokens_decoder": {
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},
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"content": "<eop>",
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},
"151335": {
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"151336": {
"content": "<|user|>",
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"151337": {
"content": "<|assistant|>",
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"single_word": false,
"special": true
},
"151338": {
"content": "<|observation|>",
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"151339": {
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},
"additional_special_tokens": [
"<|endoftext|>",
"[MASK]",
"[gMASK]",
"[sMASK]",
"<sop>",
"<eop>",
"<|system|>",
"<|user|>",
"<|assistant|>",
"<|observation|>",
"<|begin_of_image|>",
"<|end_of_image|>",
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],
"chat_template": "[gMASK]<sop>{%- if tools -%}<|system|>\n# 可用工具\n{% for tool in tools %}{%- set function = tool.function if tool.get(\"function\") else tool %}\n\n## {{ function.name }}\n\n{{ function | tojson(indent=4, ensure_ascii=False) }}\n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|>\n{{ msg.content }}{%- endif %}{%- endfor %}{%- for message in messages if message.role != 'system' %}{%- set role = message['role'] %}{%- set content = message['content'] %}{%- set meta = message.get(\"metadata\", \"\") %}{%- if role == 'user' %}<|user|>\n{{ content }}{%- elif role == 'assistant' and not meta %}<|assistant|>\n{{ content }}{%- elif role == 'assistant' and meta %}<|assistant|>{{ meta }} \n{{ content }}{%- elif role == 'observation' %}<|observation|>\n{{ content }}{%- endif %}{%- endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
"clean_up_tokenization_spaces": false,
"do_lower_case": false,
"eos_token": "<|user|>",
"extra_special_tokens": {},
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 128000,
"pad_token": "<|endoftext|>",
"padding_side": "left",
"remove_space": false,
"tokenizer_class": "PreTrainedTokenizer"
}
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