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)

# pip install -U transformers accelerate
# 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=256)
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 23

Commit

4eb8fbc

verified ·

1 Parent(s): 51a231e

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +12 -12

README.md CHANGED Viewed

@@ -49,7 +49,7 @@ tags:
 ---
-## 📊 The headline — v2 is a different model
 v2 is a full retrain: **13× more genuine Fable 5 trace data** (11,574 sessions, 16.8M tokens — [corpus published](https://huggingface.co/datasets/AnkitAI/parable-corpus-v2)) and a rebuilt recipe (completion-masked loss, replay mixing, benchmark-gated checkpoints, seed-averaged weights).
@@ -64,7 +64,7 @@ Clean answers, structured reasoning, agent instincts — and the transcript arti
 ---
-## 🚀 Announcements
 **📌 Same links, new model.** v2 replaces v1 **in place** — every existing Ollama command, script, and bookmark now serves v2. No migration, nothing to change.
@@ -74,7 +74,7 @@ Clean answers, structured reasoning, agent instincts — and the transcript arti
 ---
-## 🚀 How to run it
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
@@ -91,20 +91,20 @@ print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
 GGUF quants (2.1-6.8 GB, runs in ~3 GB RAM): [Parable-Granite-4.1-3B-Claude-Fable-5-GGUF](https://huggingface.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF)
-### 🧠 Thinking mode
 Every answer opens with a `<think>...</think>` reasoning block — that's the Fable 5 heritage. llama.cpp's `--jinja` mode separates it automatically; strip it before showing replies to end users.
 **Sampling:** temperature 0.7, top_p 0.95, and budget `max_tokens` generously (**2500+**) — trace-trained models think at length before answering.
 ---
-## 🔬 Measurement notes
 All numbers: identical llama.cpp harness, greedy decoding, Q4_K_M, **base model measured on the same instrument**. We train multiple seeds and ship the weight-average — single-run scores at 3B swing ±3 points on GPU nondeterminism alone, so most cards report their luckiest run; we ship the average and report the shipped weights' own numbers. Raw eval outputs live in this repo.
 **Which model should you use?** Pure single-function code completion → the base model is genuinely strong there. Explanations, debugging, terminal workflows, structured reasoning, agent-style tasks → that's what Parable is trained on, and where v2 shines.
-## 📚 What's new in v2 (training)
 The recipe follows our ongoing tech report (in preparation):
@@ -116,17 +116,17 @@ The recipe follows our ongoing tech report (in preparation):
 With Claude Fable 5 now retired, genuine self-authored Fable traces are a fixed, non-renewable corpus. Unlike most models in this niche, **our full training corpus is public**: [AnkitAI/parable-corpus-v2](https://huggingface.co/datasets/AnkitAI/parable-corpus-v2) — deduplicated, quality-gated, provenance-tagged.
-## ⚠️ Good to know
 - Fine-tuned at 2,048-token sequences; the base 128K context stays available, fine-tuned behavior is strongest in the opening turns.
 - 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.
-## 🪶 Get Parable
 | Platform | |
 |---|---|
@@ -135,15 +135,15 @@ Weights: **Apache-2.0** (inherited from [ibm-granite/granite-4.1-3b](https://hug
 | LM Studio | search "parable" in-app |
 | ModelScope | [Parable on ModelScope](https://modelscope.cn/models/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF) |
-## 🙏 Acknowledgements
 [Glint-Research](https://huggingface.co/Glint-Research) & [Roman1111111](https://huggingface.co/Roman1111111) for the open trace data · [IBM Granite](https://huggingface.co/ibm-granite) for the base · [empero-ai](https://huggingface.co/empero-ai) whose Qwable recipe inspired the series · [llama.cpp](https://github.com/ggml-org/llama.cpp)
-## 🗂 Version history
 - **v2** (2026-07-16) — this release. 13× corpus, rebuilt recipe, seed-averaged weights, zero leakage.
 - **v1** (2026-07) — initial release, 857-row corpus. Preserved as repo revision history.
 ---
-### 🪶 Real Fable 5 reasoning. Yours, offline, right now.

 ---
+## The headline — v2 is a different model
 v2 is a full retrain: **13× more genuine Fable 5 trace data** (11,574 sessions, 16.8M tokens — [corpus published](https://huggingface.co/datasets/AnkitAI/parable-corpus-v2)) and a rebuilt recipe (completion-masked loss, replay mixing, benchmark-gated checkpoints, seed-averaged weights).
 ---
+## Announcements
 **📌 Same links, new model.** v2 replaces v1 **in place** — every existing Ollama command, script, and bookmark now serves v2. No migration, nothing to change.
 ---
+## How to run it
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 GGUF quants (2.1-6.8 GB, runs in ~3 GB RAM): [Parable-Granite-4.1-3B-Claude-Fable-5-GGUF](https://huggingface.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF)
+### Thinking mode
 Every answer opens with a `<think>...</think>` reasoning block — that's the Fable 5 heritage. llama.cpp's `--jinja` mode separates it automatically; strip it before showing replies to end users.
 **Sampling:** temperature 0.7, top_p 0.95, and budget `max_tokens` generously (**2500+**) — trace-trained models think at length before answering.
 ---
+## Measurement notes
 All numbers: identical llama.cpp harness, greedy decoding, Q4_K_M, **base model measured on the same instrument**. We train multiple seeds and ship the weight-average — single-run scores at 3B swing ±3 points on GPU nondeterminism alone, so most cards report their luckiest run; we ship the average and report the shipped weights' own numbers. Raw eval outputs live in this repo.
 **Which model should you use?** Pure single-function code completion → the base model is genuinely strong there. Explanations, debugging, terminal workflows, structured reasoning, agent-style tasks → that's what Parable is trained on, and where v2 shines.
+## What's new in v2 (training)
 The recipe follows our ongoing tech report (in preparation):
 With Claude Fable 5 now retired, genuine self-authored Fable traces are a fixed, non-renewable corpus. Unlike most models in this niche, **our full training corpus is public**: [AnkitAI/parable-corpus-v2](https://huggingface.co/datasets/AnkitAI/parable-corpus-v2) — deduplicated, quality-gated, provenance-tagged.
+## Good to know
 - Fine-tuned at 2,048-token sequences; the base 128K context stays available, fine-tuned behavior is strongest in the opening turns.
 - 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.
+## Get Parable
 | Platform | |
 |---|---|
 | LM Studio | search "parable" in-app |
 | ModelScope | [Parable on ModelScope](https://modelscope.cn/models/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF) |
+## Acknowledgements
 [Glint-Research](https://huggingface.co/Glint-Research) & [Roman1111111](https://huggingface.co/Roman1111111) for the open trace data · [IBM Granite](https://huggingface.co/ibm-granite) for the base · [empero-ai](https://huggingface.co/empero-ai) whose Qwable recipe inspired the series · [llama.cpp](https://github.com/ggml-org/llama.cpp)
+## Version history
 - **v2** (2026-07-16) — this release. 13× corpus, rebuilt recipe, seed-averaged weights, zero leakage.
 - **v1** (2026-07) — initial release, 857-row corpus. Preserved as repo revision history.
 ---
+### Real Fable 5 reasoning. Yours, offline, right now.