Text Generation
Transformers
Safetensors
PEFT
English
lora
presentation-templates
information-retrieval
gemma
conversational
Instructions to use mudasir13cs/Field-adaptive-query-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mudasir13cs/Field-adaptive-query-generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mudasir13cs/Field-adaptive-query-generator") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mudasir13cs/Field-adaptive-query-generator", dtype="auto") - PEFT
How to use mudasir13cs/Field-adaptive-query-generator with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mudasir13cs/Field-adaptive-query-generator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mudasir13cs/Field-adaptive-query-generator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mudasir13cs/Field-adaptive-query-generator", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mudasir13cs/Field-adaptive-query-generator
- SGLang
How to use mudasir13cs/Field-adaptive-query-generator 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 "mudasir13cs/Field-adaptive-query-generator" \ --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": "mudasir13cs/Field-adaptive-query-generator", "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 "mudasir13cs/Field-adaptive-query-generator" \ --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": "mudasir13cs/Field-adaptive-query-generator", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mudasir13cs/Field-adaptive-query-generator with Docker Model Runner:
docker model run hf.co/mudasir13cs/Field-adaptive-query-generator
Add chat_template.jinja
Browse files- chat_template.jinja +47 -0
chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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