LFM2-2.6B-Transcript

2.6B parameter model for private, on-device meeting summarization

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LFM2-2.6B-Transcript is designed for private, on-device meeting summarization from transcripts. It generates executive summaries, detailed summaries, action items, key decisions, and participant lists.

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Specifications

Property	Value
Parameters	2.6B
Context Length	32K tokens
Task	Meeting Summarization
Recommended Temperature	0.3

Executive Summaries

2-3 sentence overviews

Action Items

Extract assigned tasks

Private Processing

On-device summarization

Prompting Recipe

Use temperature=0.3 for optimal results. This model requires a specific transcript format.

System Prompt:

You are an expert meeting analyst. Analyze the transcript carefully
and provide clear, accurate information based on the content.

Input Format:

<summary_type>

Title: Meeting Title
Date: Date
Time: Time
Duration: Duration
Participants: Names (Roles)

----------

**Speaker 1**: Message
**Speaker 2**: Message

Summary Types:

Type	User Prompt
Executive summary	Provide a brief executive summary (2-3 sentences) of the key outcomes and decisions.
Detailed summary	Provide a detailed summary covering all major topics, discussions, and outcomes.
Action items	List the specific action items assigned during this meeting.
Key decisions	List the key decisions that were made during this meeting.
Participants	List the participants mentioned in this transcript.
Topics discussed	List the main topics and subjects that were discussed.

Quick Start

Transformers

Install:

pip install transformers torch

Run:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LiquidAI/LFM2-2.6B-Transcript"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

system_prompt = """You are an expert meeting analyst. Analyze the transcript carefully
and provide clear, accurate information based on the content."""

user_input = """Provide a brief executive summary (2-3 sentences) of the key outcomes and decisions.

Title: Budget Planning Meeting
Date: March 15, 2024
Time: 2:00 PM
Duration: 60 minutes
Participants: Sarah Chen (Finance Director), Mike Johnson (Operations Manager)

----------

**Sarah Chen**: Good afternoon. Let's review our Q1 budget performance.
**Mike Johnson**: Operations came in 5% under budget this quarter.
**Sarah Chen**: For Q2, we need to allocate additional funds for the expansion.
**Mike Johnson**: I'll provide a detailed breakdown by next week."""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_input}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.3, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

llama.cpp

Download GGUF:

huggingface-cli download LiquidAI/LFM2-2.6B-Transcript-GGUF \
  --local-dir ./LFM2-2.6B-Transcript-GGUF

Run:

llama-cli -m ./LFM2-2.6B-Transcript-GGUF/LFM2-2.6B-Transcript-Q4_K_M.gguf \
  -sys "You are an expert meeting analyst." \
  -f meeting_transcript.txt \
  --temp 0.3

See the meeting summarization cookbook example for a complete implementation.