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2.6B parameter model for private, on-device meeting summarization
| Property | Value |
|---|---|
| Parameters | 2.6B |
| Context Length | 32K tokens |
| Task | Meeting Summarization |
| Recommended Temperature | 0.3 |
temperature=0.3. The model supports English only and is intended for single-turn conversations with the input format below.You are an expert meeting analyst. Analyze the transcript carefully and provide clear, accurate information based on the content.
<user_prompt>
Title (example: Claims Processing training module)
Date (example: July 2, 2021)
Time (example: 1:00 PM)
Duration (example: 45 minutes)
Participants (example: Julie Franco (Training Facilitator), Amanda Newman (Subject Matter Expert))
----------
**Speaker 1**: Message 1 (example: **Julie Franco**: Good morning, everyone. Thanks for joining me today.)
**Speaker 2**: Message 2 (example: **Amanda Newman**: Good morning, Julie. Happy to be here.)
etc.
<user_prompt> with one of the following prompts.
| Type | User Prompt |
|---|---|
| Executive summary | Provide a brief executive summary (2-3 sentences) of the key outcomes and decisions from this transcript. |
| Detailed summary | Provide a detailed summary of the transcript, covering all major topics, discussions, and outcomes in paragraph form. |
| Action items | List the specific action items that were assigned during this meeting. Include who is responsible for each item when mentioned. |
| Key decisions | List the key decisions that were made during this meeting. Focus on concrete decisions and outcomes. |
| Participants | List the participants mentioned in this transcript. Include their roles or titles when available. |
| Topics discussed | List the main topics and subjects that were discussed in this meeting. |
pip install "transformers>=5.2.0" torch accelerate
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 from this transcript.
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, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
print(response)
hf download LiquidAI/LFM2-2.6B-Transcript-GGUF \
--local-dir ./LFM2-2.6B-Transcript-GGUF
curl -L -o meeting_transcript.txt \
https://huggingface.co/LiquidAI/LFM2-2.6B-Transcript/resolve/main/examples/meeting1.txt
llama-cli -m ./LFM2-2.6B-Transcript-GGUF/LFM2-2.6B-Transcript-Q4_K_M.gguf \
-sys "You are an expert meeting analyst. Analyze the transcript carefully and provide clear, accurate information based on the content." \
-f meeting_transcript.txt \
--temp 0.3
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