Instructions to use ATL-Machine/affine-0KB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ATL-Machine/affine-0KB with Transformers:

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

pipe = pipeline("text-generation", model="ATL-Machine/affine-0KB")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ATL-Machine/affine-0KB")
model = AutoModelForCausalLM.from_pretrained("ATL-Machine/affine-0KB")
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

vLLM

How to use ATL-Machine/affine-0KB with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ATL-Machine/affine-0KB"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ATL-Machine/affine-0KB",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ATL-Machine/affine-0KB

SGLang

How to use ATL-Machine/affine-0KB 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 "ATL-Machine/affine-0KB" \
    --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": "ATL-Machine/affine-0KB",
		"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 "ATL-Machine/affine-0KB" \
        --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": "ATL-Machine/affine-0KB",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ATL-Machine/affine-0KB with Docker Model Runner:
```
docker model run hf.co/ATL-Machine/affine-0KB
```

affine-0KB / chat_template.jinja

wilfrid51

Initial upload

6be5d63 5 months ago

raw

history blame

2.63 kB

	{%- if tools %}
	{{- '<\|im_start\|>system\n' }}
	{%- if messages[0].role == 'system' %}
	{{- messages[0].content + '\n\n' }}
	{%- endif %}
	{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
	{%- for tool in tools %}
	{{- "\n" }}
	{{- tool \| tojson }}
	{%- endfor %}
	{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><\|im_end\|>\n" }}
	{%- else %}
	{%- if messages[0].role == 'system' %}
	{{- '<\|im_start\|>system\n' + messages[0].content + '<\|im_end\|>\n' }}
	{%- endif %}
	{%- endif %}
	{%- for message in messages %}
	{%- if message.content is string %}
	{%- set content = message.content %}
	{%- else %}
	{%- set content = '' %}
	{%- endif %}
	{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
	{{- '<\|im_start\|>' + message.role + '\n' + content + '<\|im_end\|>' + '\n' }}
	{%- elif message.role == "assistant" %}
	{{- '<\|im_start\|>' + message.role + '\n' + content }}
	{%- if message.tool_calls %}
	{%- for tool_call in message.tool_calls %}
	{%- if (loop.first and content) or (not loop.first) %}
	{{- '\n' }}
	{%- endif %}
	{%- if tool_call.function %}
	{%- set tool_call = tool_call.function %}
	{%- endif %}
	{{- '<tool_call>\n{"name": "' }}
	{{- tool_call.name }}
	{{- '", "arguments": ' }}
	{%- if tool_call.arguments is string %}
	{{- tool_call.arguments }}
	{%- else %}
	{{- tool_call.arguments \| tojson }}
	{%- endif %}
	{{- '}\n</tool_call>' }}
	{%- endfor %}
	{%- endif %}
	{{- '<\|im_end\|>\n' }}
	{%- elif message.role == "tool" %}
	{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
	{{- '<\|im_start\|>user' }}
	{%- endif %}
	{{- '\n<tool_response>\n' }}
	{{- content }}
	{{- '\n</tool_response>' }}
	{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
	{{- '<\|im_end\|>\n' }}
	{%- endif %}
	{%- endif %}
	{%- endfor %}
	{%- if add_generation_prompt %}
	{{- '<\|im_start\|>assistant\n' }}
	{%- endif %}