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)

# pip install -U transformers accelerate
# 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", device_map="auto")
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=256)
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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Reasoning-expanded sibling of Fabliq-8B-Agent-FromBase. Adds general + deep reasoning (WithinUs + Helio) on top of the FromBase agentic foundation. Mirrors 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