transformers gguf safetensors chat-summary 3B Ollama text-generation-inference trl Llama3.2 llama-cpp gguf-my-repo text-generation en dataset:prithivMLmods/Context-Based-Chat-Summary-Plus base_model:prithivMLmods/Llama-Chat-Summary-3.2-3B base_model:quantized:prithivMLmods/Llama-Chat-Summary-3.2-3B license:creativeml-openrail-m endpoints_compatible region:us conversational
---
base_model: prithivMLmods/Llama-Chat-Summary-3.2-3B
datasets:
- prithivMLmods/Context-Based-Chat-Summary-Plus
language:
- en
library_name: transformers
license: creativeml-openrail-m
pipeline_tag: text-generation
tags:
- safetensors
- chat-summary
- 3B
- Ollama
- text-generation-inference
- trl
- Llama3.2
- llama-cpp
- gguf-my-repo
---

# Triangle104/Llama-Chat-Summary-3.2-3B-Q4_K_S-GGUF
This model was converted to GGUF format from [`prithivMLmods/Llama-Chat-Summary-3.2-3B`](https://huggingface.co/prithivMLmods/Llama-Chat-Summary-3.2-3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/prithivMLmods/Llama-Chat-Summary-3.2-3B) for more details on the model.

---
Model details:
-

Llama-Chat-Summary-3.2-3B: Context-Aware Summarization Model

Llama-Chat-Summary-3.2-3B is a fine-tuned model designed for generating context-aware summaries of long conversational or text-based inputs. Built on the meta-llama/Llama-3.2-3B-Instruct foundation, this model is optimized to process structured and unstructured conversational data for summarization tasks.

 Key Features

    Conversation Summarization:
        Generates concise and meaningful summaries of long chats, discussions, or threads.

    Context Preservation:
        Maintains critical points, ensuring important details aren't omitted.

    Text Summarization:
        Works beyond chats; supports summarizing articles, documents, or reports.

    Fine-Tuned Efficiency:
        Trained with Context-Based-Chat-Summary-Plus dataset for accurate summarization of chat and conversational data.

Training Details

    Base Model: meta-llama/Llama-3.2-3B-Instruct
    Fine-Tuning Dataset: prithivMLmods/Context-Based-Chat-Summary-Plus
        Contains 98.4k structured and unstructured conversations, summaries, and contextual inputs for robust training.

Applications

    Customer Support Logs:
        Summarize chat logs or support tickets for insights and reporting.

    Meeting Notes:
        Generate concise summaries of meeting transcripts.

    Document Summarization:
        Create short summaries for lengthy reports or articles.

    Content Generation Pipelines:
        Automate summarization for newsletters, blogs, or email digests.

    Context Extraction for AI Systems:
        Preprocess chat or conversation logs for downstream AI applications.

Load the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Llama-Chat-Summary-3.2-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Generate a Summary

prompt = """
Summarize the following conversation:
User1: Hey, I need help with my order. It hasn't arrived yet.
User2: I'm sorry to hear that. Can you provide your order number?
User1: Sure, it's 12345.
User2: Let me check... It seems there was a delay. It should arrive tomorrow.
User1: Okay, thank you!
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)

summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Summary:", summary)

Expected Output

"The user reported a delayed order (12345), and support confirmed it will arrive tomorrow."
Deployment Notes

    Serverless API:
    This model currently lacks sufficient usage for serverless endpoints. Use dedicated endpoints for deployment.

    Performance Requirements:
        GPU with sufficient memory (recommended for large models).
        Optimization techniques like quantization can improve efficiency for inference.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Llama-Chat-Summary-3.2-3B-Q4_K_S-GGUF --hf-file llama-chat-summary-3.2-3b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama-Chat-Summary-3.2-3B-Q4_K_S-GGUF --hf-file llama-chat-summary-3.2-3b-q4_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Llama-Chat-Summary-3.2-3B-Q4_K_S-GGUF --hf-file llama-chat-summary-3.2-3b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama-Chat-Summary-3.2-3B-Q4_K_S-GGUF --hf-file llama-chat-summary-3.2-3b-q4_k_s.gguf -c 2048
```
