Instructions to use playforgecoding/LFM2-1.2B-Extract-lesson with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use playforgecoding/LFM2-1.2B-Extract-lesson with Transformers:

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

pipe = pipeline("text-generation", model="playforgecoding/LFM2-1.2B-Extract-lesson")
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("playforgecoding/LFM2-1.2B-Extract-lesson")
model = AutoModelForCausalLM.from_pretrained("playforgecoding/LFM2-1.2B-Extract-lesson", 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 playforgecoding/LFM2-1.2B-Extract-lesson with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "playforgecoding/LFM2-1.2B-Extract-lesson"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "playforgecoding/LFM2-1.2B-Extract-lesson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/playforgecoding/LFM2-1.2B-Extract-lesson

SGLang

How to use playforgecoding/LFM2-1.2B-Extract-lesson 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 "playforgecoding/LFM2-1.2B-Extract-lesson" \
    --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": "playforgecoding/LFM2-1.2B-Extract-lesson",
		"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 "playforgecoding/LFM2-1.2B-Extract-lesson" \
        --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": "playforgecoding/LFM2-1.2B-Extract-lesson",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use playforgecoding/LFM2-1.2B-Extract-lesson with Docker Model Runner:
```
docker model run hf.co/playforgecoding/LFM2-1.2B-Extract-lesson
```

LFM2-1.2B-Extract-lesson / chat_template.jinja

playforgecoding

Upload tokenizer

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{{- bos_token -}}{%- set system_prompt = "" -%}{%- set ns = namespace(system_prompt="") -%}{%- if messages[0]["role"] == "system" -%} {%- set ns.system_prompt = messages[0]["content"] -%} {%- set messages = messages[1:] -%}{%- endif -%}{%- if tools -%} {%- set ns.system_prompt = ns.system_prompt + ("
" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%} {%- for tool in tools -%} {%- if tool is not string -%} {%- set tool = tool | tojson -%} {%- endif -%} {%- set ns.system_prompt = ns.system_prompt + tool -%} {%- if not loop.last -%} {%- set ns.system_prompt = ns.system_prompt + ", " -%} {%- endif -%} {%- endfor -%} {%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%}{%- endif -%}{%- if ns.system_prompt -%} {{- "<|im_start|>system
" + ns.system_prompt + "<|im_end|>
" -}}{%- endif -%}{%- for message in messages -%} {{- "<|im_start|>" + message["role"] + "
" -}} {%- set content = message["content"] -%} {%- if content is not string -%} {%- set content = content | tojson -%} {%- endif -%} {%- if message["role"] == "tool" -%} {%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%} {%- endif -%} {{- content + "<|im_end|>
" -}}{%- endfor -%}{%- if add_generation_prompt -%} {{- "<|im_start|>assistant
" -}}{%- endif -%}

	{{- bos_token -}}{%- set system_prompt = "" -%}{%- set ns = namespace(system_prompt="") -%}{%- if messages[0]["role"] == "system" -%} {%- set ns.system_prompt = messages[0]["content"] -%} {%- set messages = messages[1:] -%}{%- endif -%}{%- if tools -%} {%- set ns.system_prompt = ns.system_prompt + ("
	" if ns.system_prompt else "") + "List of tools: <\|tool_list_start\|>[" -%} {%- for tool in tools -%} {%- if tool is not string -%} {%- set tool = tool \| tojson -%} {%- endif -%} {%- set ns.system_prompt = ns.system_prompt + tool -%} {%- if not loop.last -%} {%- set ns.system_prompt = ns.system_prompt + ", " -%} {%- endif -%} {%- endfor -%} {%- set ns.system_prompt = ns.system_prompt + "]<\|tool_list_end\|>" -%}{%- endif -%}{%- if ns.system_prompt -%} {{- "<\|im_start\|>system
	" + ns.system_prompt + "<\|im_end\|>
	" -}}{%- endif -%}{%- for message in messages -%} {{- "<\|im_start\|>" + message["role"] + "
	" -}} {%- set content = message["content"] -%} {%- if content is not string -%} {%- set content = content \| tojson -%} {%- endif -%} {%- if message["role"] == "tool" -%} {%- set content = "<\|tool_response_start\|>" + content + "<\|tool_response_end\|>" -%} {%- endif -%} {{- content + "<\|im_end\|>
	" -}}{%- endfor -%}{%- if add_generation_prompt -%} {{- "<\|im_start\|>assistant
	" -}}{%- endif -%}