Instructions to use Feudor2/hallucination_detector_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Feudor2/hallucination_detector_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Feudor2/hallucination_detector_v3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Feudor2/hallucination_detector_v3") model = AutoModelForMultimodalLM.from_pretrained("Feudor2/hallucination_detector_v3") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Feudor2/hallucination_detector_v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Feudor2/hallucination_detector_v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Feudor2/hallucination_detector_v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Feudor2/hallucination_detector_v3
- SGLang
How to use Feudor2/hallucination_detector_v3 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 "Feudor2/hallucination_detector_v3" \ --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": "Feudor2/hallucination_detector_v3", "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 "Feudor2/hallucination_detector_v3" \ --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": "Feudor2/hallucination_detector_v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Feudor2/hallucination_detector_v3 with Docker Model Runner:
docker model run hf.co/Feudor2/hallucination_detector_v3
File size: 1,989 Bytes
6ce8db3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | <s>{%- set names = {'assistant': ' Ассистент:', 'user': ' Пользователь:'} %}
{%- set tools_prefix = 'Тебе доступны следующие функции:' %}
{%- macro __render_tool(tool) %}
{%- set name = tool.function.name %}
{%- set description = tool.function.description|default('') %}
{%- set parameters = tool.function.parameters|tojson %}
{{- '\n' }}function {{ '{' }}'name':'{{ name }}',
{%- if tool.description %}'description':'{{ description }}',{% endif %}
'parameters':{{ parameters }}
{{- '}' }}
{%- endmacro %}
{%- macro __render_tools(tools) %}
{{- tools_prefix }}
{%- for tool in tools %}
{{- __render_tool(tool) }}
{%- endfor %}
{{- '\n\n' }}
{%- endmacro %}
{%- macro __render_tool_message(message) %}
{{- '\n\nРезультат вызова' }} {{ message.name }}: {{ message.content }} {{ '\n\n' }}
{%- endmacro %}
{%- if tools -%}
{{- __render_tools(tools) }}
{%- endif -%}
{%- macro __render_user_message(message) %}
{{ names.user }} {{ message.content + '\n\n' }}
{%- endmacro %}
{%- macro __render_assistant_message(message) %}
{{- names.assistant }}
{%- set call = message['function_call'] %}
{%- if call %}
{{- '\n[TOOL_CALL_START]' }}{{ call.name }}{{ '\n' }}{{ call.arguments|tojson }}
{%- else %}
{{- ' ' + message.content + '\n\n' }}
{%- endif %}
{%- endmacro %}
{%- if not add_generation_prompt is defined %}
{%- set add_generation_prompt = false %}
{%- endif %}
{%- for message in messages %}
{%- if message['role'] == 'user' %}
{{- __render_user_message(message) }}
{%- endif %}
{%- if message.role == 'assistant' and not loop.last %}
{{- __render_assistant_message(message) }}
{%- endif %}
{%- if message.role == 'tool' %}
{{- __render_tool_message(message) }}
{%- endif %}
{%- if loop.last %}
{{- ' Ассистент:[SEP]' }}
{%- endif %}
{%- endfor %}
|