> ## Documentation Index
> Fetch the complete documentation index at: https://docs.liquid.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
## Specifications
| Property | Value |
| ----------------------- | --------------------- |
| Parameters | 2.6B |
| Context Length | 32K tokens |
| Task | Meeting Summarization |
| Recommended Temperature | 0.3 |
2-3 sentence overviews
Extract assigned tasks
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:**
```
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
**Install:**
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
pip install "transformers>=5.2.0" torch accelerate
```
**Run:**
```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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", 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], skip_special_tokens=True)
print(response)
```
**Download GGUF:**
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
hf download LiquidAI/LFM2-2.6B-Transcript-GGUF \
--local-dir ./LFM2-2.6B-Transcript-GGUF
```
**Run:**
```bash theme={"theme":{"light":"github-light","dark":"github-dark"}}
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](https://github.com/Liquid4All/cookbook/tree/main/examples/meeting-summarization) for a complete implementation.