Instructions to use fableforge-ai/FableForge-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fableforge-ai/FableForge-14B with Transformers:

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

pipe = pipeline("text-generation", model="fableforge-ai/FableForge-14B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("fableforge-ai/FableForge-14B")
model = AutoModelForMultimodalLM.from_pretrained("fableforge-ai/FableForge-14B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fableforge-ai/FableForge-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fableforge-ai/FableForge-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/FableForge-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/fableforge-ai/FableForge-14B

SGLang

How to use fableforge-ai/FableForge-14B 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 "fableforge-ai/FableForge-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/FableForge-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "fableforge-ai/FableForge-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fableforge-ai/FableForge-14B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use fableforge-ai/FableForge-14B with Docker Model Runner:
```
docker model run hf.co/fableforge-ai/FableForge-14B
```

Configuration Parsing Warning:In UNKNOWN_FILENAME: "tokenizer_config.bos_token.__type" is required

Configuration Parsing Warning:In UNKNOWN_FILENAME: "tokenizer_config.eos_token.__type" is required

Configuration Parsing Warning:In UNKNOWN_FILENAME: "tokenizer_config.unk_token.__type" is required

Configuration Parsing Warning:In UNKNOWN_FILENAME: "tokenizer_config.pad_token.__type" is required

FableForge-14B

A 14B parameter agent orchestration model fine-tuned for multi-step reasoning, tool use, and autonomous task execution. Trained on Fable5 agent traces and curated reasoning datasets.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "fableforge-ai/FableForge-14B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = """You are an AI agent. Complete the following task:

Task: Write a Python function to calculate the Fibonacci sequence.

Reasoning:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Use Cases

Multi-step agent planning and execution
Tool selection and API orchestration
Code generation with verification loops
Autonomous debugging and error recovery

Integration with FableForge Ecosystem

from fableforge_agent_runtime import AgentRuntime
from fableforge_agent_skills import SkillLibrary

runtime = AgentRuntime(
    model="fableforge-ai/FableForge-14B",
    skills=SkillLibrary.all(),
    verification=True
)

result = runtime.run("Deploy a web server on AWS")
print(result.output)
print(result.verification_score)

Ecosystem Integration

Part of the FableForge Agent Ecosystem - 21 open-source projects for building, testing, and deploying AI agents.

Package	Install	Purpose
`fableforge`	`pip install fableforge`	Unified CLI
`fableforge-anvil-agent`	`pip install fableforge-anvil-agent`	Self-verified coding agent
`fableforge-agent-swarm`	`pip install fableforge-agent-swarm`	Multi-agent orchestration
`fableforge-agent-runtime`	`pip install fableforge-agent-runtime`	Production agent runtime
`fableforge-agent-skills`	`pip install fableforge-agent-skills`	Skill library
`verifyloop`	`pip install verifyloop`	Verification loops
`reason-critic`	`pip install reason-critic`	Reasoning assessment

Model Details

Attribute	Value
Architecture	LlamaForCausalLM
Parameters	14B
Hidden Size	5120
Layers	40
Attention Heads	40
KV Heads	40
Max Context	4096
Training Data	Fable5 agent traces + curated reasoning datasets
License	MIT

Limitations

May generate incorrect code -- always use with verifyloop for critical tasks
Trained primarily on English data; multilingual performance is limited
Can hallucinate API signatures or tool parameters
Not suitable for medical, legal, or financial advice without human review

Citation

@misc{fableforge14b2024,
  title={FableForge-14B: Agent Orchestration via Fine-Tuned Language Models},
  author={FableForge Team},
  year={2024},
  url={https://huggingface.co/fableforge-ai/FableForge-14B}
}

License

MIT License - see LICENSE for details.

Built with hammer by the FableForge team. Part of the FableForge ecosystem.

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