Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
conversational
4-bit precision
bitsandbytes
Instructions to use muhammadocama/ClinGuard-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use muhammadocama/ClinGuard-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="muhammadocama/ClinGuard-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("muhammadocama/ClinGuard-4bit") model = AutoModelForCausalLM.from_pretrained("muhammadocama/ClinGuard-4bit") 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 muhammadocama/ClinGuard-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "muhammadocama/ClinGuard-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "muhammadocama/ClinGuard-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/muhammadocama/ClinGuard-4bit
- SGLang
How to use muhammadocama/ClinGuard-4bit 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 "muhammadocama/ClinGuard-4bit" \ --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": "muhammadocama/ClinGuard-4bit", "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 "muhammadocama/ClinGuard-4bit" \ --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": "muhammadocama/ClinGuard-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use muhammadocama/ClinGuard-4bit with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for muhammadocama/ClinGuard-4bit to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for muhammadocama/ClinGuard-4bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for muhammadocama/ClinGuard-4bit to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="muhammadocama/ClinGuard-4bit", max_seq_length=2048, ) - Docker Model Runner
How to use muhammadocama/ClinGuard-4bit with Docker Model Runner:
docker model run hf.co/muhammadocama/ClinGuard-4bit
| {{- bos_token }} | |
| {%- if messages[0]['role'] == 'system' -%}{%- set system_message = messages[0]['content'] | trim -%}{%- set messages = messages[1:] -%}{%- else -%}{%- set system_message = '' -%}{%- endif -%}{%- if tools is not none -%}{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>' + ' | |
| ' + system_message -}} {{- ' | |
| ' if system_message else '' -}} {{- '<AVAILABLE_TOOLS>[' -}} {% for t in tools %}{{- (t.function if t.function is defined else t) | tojson() -}}{{- ', ' if not loop.last else '' -}}{%- endfor -%} {{- ']</AVAILABLE_TOOLS>' -}} {{- '<|eot_id|>' -}}{%- else -%}{{- '<|begin_of_text|><|start_header_id|>system<|end_header_id|>' + ' | |
| ' + system_message + '<|eot_id|>' -}}{%- endif -%}{%- for message in messages -%}{%- if (message['role'] in ['user', 'tool']) != (loop.index0 % 2 == 0) -%}{{- raise_exception('Conversation roles must alternate between user/tool and assistant') -}}{%- elif message['role'] == 'user' -%}{{- '<|start_header_id|>user<|end_header_id|>' + ' | |
| ' + message['content'] | trim + '<|eot_id|>' -}}{%- elif message['role'] == 'tool' -%}{%- set tool_response = '<TOOL_RESPONSE>[' + message['content'] | trim + ']</TOOL_RESPONSE>' -%}{{- '<|start_header_id|>user<|end_header_id|>' + ' | |
| ' + tool_response + '<|eot_id|>' -}}{%- elif message['role'] == 'assistant' and message.get('tool_calls') is not none -%}{%- set tool_calls = message['tool_calls'] -%}{{- '<|start_header_id|>assistant<|end_header_id|>' + ' | |
| ' + '<TOOLCALL>[' -}}{%- for tool_call in tool_calls -%}{{ '{' + '"name": "' + tool_call.function.name + '", "arguments": ' + tool_call.function.arguments | tojson + '}' }}{%- if not loop.last -%}{{ ', ' }}{%- else -%}{{ ']</TOOLCALL>' + '<|eot_id|>' }}{%- endif -%}{%- endfor -%}{%- elif message['role'] == 'assistant' -%}{{- '<|start_header_id|>assistant<|end_header_id|>' + ' | |
| ' + message['content'] | trim + '<|eot_id|>' -}}{%- endif -%}{%- endfor -%}{%- if add_generation_prompt -%}{{ '<|start_header_id|>assistant<|end_header_id|>' + ' | |
| ' }}{%- endif -%} |