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
```

gyung commited on 26 days ago

Commit

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+---
+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](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent) — fine-tuned directly from raw [`LiquidAI/LFM2.5-8B-A1B`](https://huggingface.co/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](https://huggingface.co/LLM-OS-Models/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](https://huggingface.co/datasets/Glint-Research/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](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent) — same system prompt, same tool-call format, same inference code.
+## 📜 License
+Apache 2.0.