Instructions to use LLM-OS-Models/Fabliq-8B-Agent-FromBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/Fabliq-8B-Agent-FromBase with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLM-OS-Models/Fabliq-8B-Agent-FromBase")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/Fabliq-8B-Agent-FromBase")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/Fabliq-8B-Agent-FromBase")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM-OS-Models/Fabliq-8B-Agent-FromBase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/Fabliq-8B-Agent-FromBase"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Fabliq-8B-Agent-FromBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/Fabliq-8B-Agent-FromBase

SGLang

How to use LLM-OS-Models/Fabliq-8B-Agent-FromBase with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LLM-OS-Models/Fabliq-8B-Agent-FromBase" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Fabliq-8B-Agent-FromBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LLM-OS-Models/Fabliq-8B-Agent-FromBase" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Fabliq-8B-Agent-FromBase",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/Fabliq-8B-Agent-FromBase with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/Fabliq-8B-Agent-FromBase
```

Fabliq-8B-Agent-FromBase / README.md

gyung

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metadata

library_name: transformers
license: apache-2.0
base_model: LiquidAI/LFM2.5-8B-A1B
base_model_relation: finetune
tags:
  - lfm
  - liquid-ai
  - moe
  - agentic
  - terminal
  - fable-5
  - distillation
  - sft
  - ablation
language:
  - en
pipeline_tag: text-generation

Fabliq-8B-Agent-FromBase 🌊🔬

Ablation variant of Fabliq-8B-Agent — fine-tuned directly from raw LiquidAI/LFM2.5-8B-A1B, skipping the ToolBench foundation. Used to isolate the effect of the ToolBench intermediate stage.

🔬 Why this variant?

Fabliq-8B-Agent is trained as: LiquidAI/LFM2.5-8B-A1B → ToolBench-Full-SFT-1Epoch → Fable-5.

This model skips the middle step: LiquidAI/LFM2.5-8B-A1B → Fable-5 (direct).

Comparing the two answers the question: does ToolBench foundation actually help, or does Fable-5 alone give you the same agent?

🧪 Model details


Architecture	Lfm2MoeForCausalLM (24 layers, 32 experts, 4 experts/token)
Parameters	~8B total / ~1B active (MoE)
Context	8,192 trained · 128K native
Precision	bfloat16
Fine-tune type	Full-parameter SFT (direct from base, no ToolBench)
License	Apache 2.0

📚 Training data

Same as Phase-1: Fable-5-traces, 4,047 rows × 3 epoch.

🔧 Training procedure

Hyperparameter	Value
Schedule	3 epochs, constant LR
Max sequence length	8,192
Per-device batch size	2
Gradient accumulation	4
GPUs	8× H200 (effective batch 64)
Learning rate	1e-6 (higher than Phase-1's 5e-7, since starting from base)
Precision	bf16
Final train_loss	logged in run_config.json
Train runtime	~14 min

🌳 Model tree

LiquidAI/LFM2.5-8B-A1B
  ├─ LLM-OS-Models/LFM2.5-8B-A1B-Terminal-ToolBench-Full-SFT-1Epoch
  │   └─ LLM-OS-Models/Fabliq-8B-Agent          ← Phase-1 (ToolBench → Fable-5)
  └─ LLM-OS-Models/Fabliq-8B-Agent-FromBase     ← this model (base → Fable-5, direct)
      └─ LLM-OS-Models/Fabliq-8B-Agent-FromBase-Reasoning  ← + reasoning expansion

🚀 Usage

See Fabliq-8B-Agent — same system prompt, same tool-call format, same inference code.

📜 License

Apache 2.0.