Instructions to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with Transformers:

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

pipe = pipeline("text-generation", model="AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5")
model = AutoModelForCausalLM.from_pretrained("AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5

SGLang

How to use AnkitAI/Parable-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-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-Qwen3-8B-Claude-Fable-5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AnkitAI/Parable-Qwen3-8B-Claude-Fable-5 with Docker Model Runner:
```
docker model run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5
```

AnkitAI commited on 8 days ago

Commit

7d5420f

verified ·

1 Parent(s): cde2580

Keep one support section, above the citation

Browse files

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README.md +0 -14

README.md CHANGED Viewed

@@ -74,7 +74,6 @@ Held-out test split, identical evaluation code and context length for base and f
 For reference, the strongest published fine-tune on this data family (a 9B) reports 0.71 validation loss. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).
 ### Function calling (BFCL V3, AST subset)
 Measured 2026-07-29: bfcl-eval at gorilla main, prompting mode, Q4_K_M
@@ -105,7 +104,6 @@ As a fine-tune it inherits Qwen3-8B's base behaviors and knowledge cutoff. As wi
 Model weights: **Apache-2.0** (inherited from Qwen3-8B). Training data licenses: Fable-5-traces **AGPL-3.0**, gpt5.5-terminal **MIT**. Because those traces originate from third-party assistants, the providers' terms may apply to downstream training and distillation. If you plan to build on this model commercially, confirm your use aligns with those terms.
 ## Get Parable
 | Platform | Command / Link |
@@ -150,15 +148,3 @@ documented in the tech report:
 - [llama.cpp](https://github.com/ggml-org/llama.cpp)
 More on the Parable models: [ankitaglawe.com/parable](https://ankitaglawe.com/parable)
----
-## Support the Project
-If these models help your research or products, consider supporting independent research:
-<p align="left">
-  <a href="https://www.buymeacoffee.com/AnkitAI" target="_blank">
-    <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me a Coffee" height="60" width="217" />
-  </a>
-</p>

 For reference, the strongest published fine-tune on this data family (a 9B) reports 0.71 validation loss. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).
 ### Function calling (BFCL V3, AST subset)
 Measured 2026-07-29: bfcl-eval at gorilla main, prompting mode, Q4_K_M
 Model weights: **Apache-2.0** (inherited from Qwen3-8B). Training data licenses: Fable-5-traces **AGPL-3.0**, gpt5.5-terminal **MIT**. Because those traces originate from third-party assistants, the providers' terms may apply to downstream training and distillation. If you plan to build on this model commercially, confirm your use aligns with those terms.
 ## Get Parable
 | Platform | Command / Link |
 - [llama.cpp](https://github.com/ggml-org/llama.cpp)
 More on the Parable models: [ankitaglawe.com/parable](https://ankitaglawe.com/parable)