Text Generation
Transformers
Safetensors
English
qwen3
qlora
agentic
coding
reasoning
thinking
claude
conversational
text-generation-inference
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
- Transformers
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
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| Hugging Face | [GGUF quants, full weights, eval reports](https://huggingface.co/collections/AnkitAI/parable-6a4fac60f4b35afca3019621) |
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| LM Studio | `lms get parable/qwen3-fable` ([parable on LM Studio Hub](https://lmstudio.ai/parable)) |
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## Citation
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The recipe, evaluation methodology and failure analysis behind this model are
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| Hugging Face | [GGUF quants, full weights, eval reports](https://huggingface.co/collections/AnkitAI/parable-6a4fac60f4b35afca3019621) |
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| LM Studio | `lms get parable/qwen3-fable` ([parable on LM Studio Hub](https://lmstudio.ai/parable)) |
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## Support the Project
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If this model is useful in your work, you can support independent research:
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<p align="left">
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<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>
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</p>
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## Citation
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The recipe, evaluation methodology and failure analysis behind this model are
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