Instructions to use LLM-OS-Models/Fabliq-8B-Agent-FromBase-Reasoning 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-Reasoning 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-Reasoning")
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-Reasoning")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/Fabliq-8B-Agent-FromBase-Reasoning")
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-Reasoning 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-Reasoning"
# 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-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use LLM-OS-Models/Fabliq-8B-Agent-FromBase-Reasoning 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-Reasoning" \
    --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-Reasoning",
		"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-Reasoning" \
        --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-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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

gyung

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	---
	library_name: transformers
	license: apache-2.0
	base_model: LLM-OS-Models/Fabliq-8B-Agent-FromBase
	base_model_relation: finetune
	tags:
	- lfm
	- liquid-ai
	- moe
	- agentic
	- terminal
	- reasoning
	- fable-5
	- withinus
	- helio
	- distillation
	- sft
	- ablation
	language:
	- en
	pipeline_tag: text-generation
	---

	# Fabliq-8B-Agent-FromBase-Reasoning 🌊🔬🧠

	> Reasoning-expanded sibling of [Fabliq-8B-Agent-FromBase](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent-FromBase). Adds general + deep reasoning (WithinUs + Helio) on top of the FromBase agentic foundation. Mirrors [Fabliq-8B-Agent-Reasoning](https://huggingface.co/LLM-OS-Models/Fabliq-8B-Agent-Reasoning) but for the FromBase ablation lineage.

	## 🔬 Why this variant?

	Two parallel lineages, both end with reasoning expansion:

	\| Lineage \| Phase-1 \| Phase-2 \|
	\| --- \| --- \| --- \|
	\| ToolBench line \| `Fabliq-8B-Agent` (ToolBench → Fable-5) \| `Fabliq-8B-Agent-Reasoning` \|
	\| FromBase line (this) \| `Fabliq-8B-Agent-FromBase` (base → Fable-5) \| `Fabliq-8B-Agent-FromBase-Reasoning` (this) \|

	Comparing the two Phase-2 endpoints answers: does ToolBench foundation matter for the final reasoning model, or is Fable-5 + reasoning enough?

	## 🧪 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 continuation \|
	\| License \| Apache 2.0 \|

	## 📚 Training data (Phase-2B only)

	Same as Phase-2: WithinUs 135 + Helio 146 = 281 rows × 4 epoch.

	## 🔧 Training procedure

	\| Hyperparameter \| Value \|
	\| --- \| --- \|
	\| Base \| `LLM-OS-Models/Fabliq-8B-Agent-FromBase` \|
	\| Schedule \| 4 epochs, LR 3e-7 \|
	\| Max sequence length \| 8,192 \|
	\| GPUs \| 8× H200 (effective batch 64) \|
	\| Train runtime \| ~6 min \|

	## 🌳 Model tree

	```
	LiquidAI/LFM2.5-8B-A1B
	├─ ToolBench line:
	│ └─ LFM2.5-8B-A1B-Terminal-ToolBench-Full-SFT-1Epoch
	│ └─ Fabliq-8B-Agent
	│ └─ Fabliq-8B-Agent-Reasoning
	└─ FromBase line (this):
	└─ Fabliq-8B-Agent-FromBase
	└─ Fabliq-8B-Agent-FromBase-Reasoning ← this model
	```

	## 📜 License

	Apache 2.0.