Instructions to use AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5 with Transformers:

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

pipe = pipeline("text-generation", model="AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5")
model = AutoModelForCausalLM.from_pretrained("AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5

SGLang

How to use AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5 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 "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5" \
    --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": "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5",
		"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 "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5" \
        --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": "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5 with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5
```

AnkitAI commited on Jul 30

Commit

b25cef4

verified ·

1 Parent(s): 606c0ed

Add measured BFCL V3 AST results (base vs Parable, identical harness)

Browse files

Files changed (1) hide show

README.md +25 -0

README.md CHANGED Viewed

@@ -122,6 +122,31 @@ With Claude Fable 5 now retired, genuine self-authored Fable traces are a fixed,
 - Not trained for: multi-file repo navigation, vision, non-English.
 - Inherits Granite-4.1-3B's knowledge cutoff. Treat generated commands as drafts to review.
 ## Base & license
 Weights: **Apache-2.0** (inherited from [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b)). Training data: Fable-5-traces **AGPL-3.0**, gpt5.5-terminal **MIT** — since traces originate from third-party assistants, their terms may apply to downstream training; check before commercial distillation.

 - Not trained for: multi-file repo navigation, vision, non-English.
 - Inherits Granite-4.1-3B's knowledge cutoff. Treat generated commands as drafts to review.
+## Evaluation
+### Function calling (BFCL V3, AST subset)
+Measured 2026-07-29: bfcl-eval at gorilla main, prompting mode, Q4_K_M
+GGUFs served by llama.cpp on a T4, base and Parable under the identical
+harness. Categories: simple_python / multiple / parallel /
+parallel_multiple (400/200/200/200 items). Raw generations and score
+files: [parable-v2-artifacts](https://huggingface.co/AnkitAI/parable-v2-artifacts)
+under `verify/bfcl/`.
+| | simple_python | multiple | parallel | parallel_multiple |
+|---|---|---|---|---|
+| Granite-4.1-3B base | 0.848 | 0.790 | 0.710 | 0.665 |
+| **This model (chat variant)** | 0.413 | 0.605 | 0.320 | 0.425 |
+**Plain reading: do not use this variant for function calling.** The
+drop is not random wrongness: sampled generations show the model
+intermittently answering with args-only tool-call JSON (for example
+`{"base": 10, "height": 5}`) instead of a function call, which the
+AST scorer rejects. That is trace-scaffolding format bleeding into
+standalone tasks, the failure mode the series paper names *session
+leakage* (Section 6 of the report). Use the base model for tool-calling
+workloads; use this variant for its reasoning prose.
 ## Base & license
 Weights: **Apache-2.0** (inherited from [ibm-granite/granite-4.1-3b](https://huggingface.co/ibm-granite/granite-4.1-3b)). Training data: Fable-5-traces **AGPL-3.0**, gpt5.5-terminal **MIT** — since traces originate from third-party assistants, their terms may apply to downstream training; check before commercial distillation.