Instructions to use torchsight/beam-q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use torchsight/beam-q8_0 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="torchsight/beam-q8_0",
	filename="beam-1.0-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use torchsight/beam-q8_0 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf torchsight/beam-q8_0:Q8_0
# Run inference directly in the terminal:
llama-cli -hf torchsight/beam-q8_0:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf torchsight/beam-q8_0:Q8_0
# Run inference directly in the terminal:
llama-cli -hf torchsight/beam-q8_0:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf torchsight/beam-q8_0:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf torchsight/beam-q8_0:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf torchsight/beam-q8_0:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf torchsight/beam-q8_0:Q8_0

Use Docker

docker model run hf.co/torchsight/beam-q8_0:Q8_0

LM Studio
Jan
Ollama
How to use torchsight/beam-q8_0 with Ollama:
```
ollama run hf.co/torchsight/beam-q8_0:Q8_0
```

Unsloth Studio new

How to use torchsight/beam-q8_0 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for torchsight/beam-q8_0 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for torchsight/beam-q8_0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for torchsight/beam-q8_0 to start chatting

Pi new

How to use torchsight/beam-q8_0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf torchsight/beam-q8_0:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "torchsight/beam-q8_0:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use torchsight/beam-q8_0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf torchsight/beam-q8_0:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default torchsight/beam-q8_0:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use torchsight/beam-q8_0 with Docker Model Runner:
```
docker model run hf.co/torchsight/beam-q8_0:Q8_0
```

Lemonade

How to use torchsight/beam-q8_0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull torchsight/beam-q8_0:Q8_0

Run and chat with the model

lemonade run user.beam-q8_0-Q8_0

List all available models

lemonade list

TorchSight Beam q8_0

Cybersecurity document classifier. LoRA fine-tune of Qwen 3.5 27B, quantized to q8_0. Approximately 28 GB GGUF.

Recommended hardware: 48 GB+ GPU or 64 GB unified memory Mac.

Higher precision than the default q4_K_M, with slightly better subcategory adherence. Released alongside:

Dobrovolskyi, I. Security Document Classification with a Fine-Tuned Local Large Language Model: Benchmark Data and an Open-Source System. Journal of Information Security and Applications, 2026.

Benchmark results

Evaluated under identical methodology (alpaca prompt, Ollama /api/generate, temperature = 0, num_predict = 2048) on the companion dataset torchsight/cybersecurity-classification-benchmark. Canonical numbers live in that repo's BENCHMARK_NUMBERS.md.

Primary — eval-1000-synthetic (n = 1,000)

Model	Type	Cat. acc [95% CI]	Subcat. acc
Beam q4_K_M	Local (LoRA)	95.0% [93.5, 96.2]	48.2%
Beam f16	Local (LoRA)	93.2% [91.5, 94.6]	51.1%
Beam q8_0	Local (LoRA)	93.0% [91.2, 94.4]	51.4%
Claude Sonnet 4	Commercial API	79.9% [77.3, 82.3]	23.0%
Claude Opus 4	Commercial API	79.9% [77.3, 82.3]	22.5%
GPT-5	Commercial API	76.9% [74.2, 79.4]	11.6%
Gemini 2.5 Pro	Commercial API	75.4% [72.6, 78.0]	21.0%
Qwen 3.5 27B base	Local (no LoRA)	86.3% [84.0, 88.3]	19.0%
Regex (48 patterns)	Rule-based	52.7% [49.6, 55.8]	—

q8_0 achieves the highest subcategory accuracy (51.4%) of the three Beam variants but slightly lower category-level accuracy than q4_K_M.

External — eval-500-external (n = 500)

Model	Cat. acc [95% CI]	Δ vs. primary
Beam q4_K_M	93.8% [91.3, 95.6]	−1.2 pp
Beam f16	91.2% [88.4, 93.4]	−2.0 pp
Beam q8_0	91.2% [88.4, 93.4]	−1.8 pp
Claude Sonnet 4	86.4% [83.1, 89.1]	+6.5 pp
Gemini 2.5 Pro	82.0% [78.4, 85.1]	+6.6 pp
Qwen 3.5 27B base	86.6% [83.3, 89.3]	+0.3 pp
GPT-5	65.8% [61.5, 69.8]	−11.1 pp
Regex baseline	29.6% [25.8, 33.7]	−23.1 pp

Usage with Ollama

ollama pull torchsight/beam-q8_0
ollama run torchsight/beam-q8_0

Or via the TorchSight CLI.

Training

Base: Qwen 3.5 27B (dense)
Method: LoRA (r = 128, α = 256), bf16, 5 epochs
Dataset: 78,358 balanced samples — see torchsight/beam-training-data
Hardware: 8× NVIDIA A100 80GB SXM4, 10.5 hours

License

Apache 2.0. The base model (Qwen 3.5 27B) carries its own license; consult upstream terms for use.

Downloads last month: 9

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

8-bit

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