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
```

gyung commited on Jun 23

Commit

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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.