Instructions to use AliesTaha/fable-traces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AliesTaha/fable-traces with Transformers:

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

pipe = pipeline("text-generation", model="AliesTaha/fable-traces")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AliesTaha/fable-traces")
model = AutoModelForCausalLM.from_pretrained("AliesTaha/fable-traces", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AliesTaha/fable-traces with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AliesTaha/fable-traces

SGLang

How to use AliesTaha/fable-traces 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 "AliesTaha/fable-traces" \
    --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": "AliesTaha/fable-traces",
		"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 "AliesTaha/fable-traces" \
        --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": "AliesTaha/fable-traces",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AliesTaha/fable-traces with Docker Model Runner:
```
docker model run hf.co/AliesTaha/fable-traces
```

Update README.md

#16

by oberonism - opened Jul 7

base: refs/heads/main

←

from: refs/pr/16

Discussion Files changed

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Files changed (1) hide show

README.md +6 -7

README.md CHANGED Viewed

@@ -2,14 +2,13 @@
 license: apache-2.0
 base_model: Qwen/Qwen3-4B-Instruct-2507
 language:
-  - en
-pipeline_tag: text-generation
 library_name: transformers
 tags:
-  - qwen3
-  - instruct
-  - conversational
-  - egypt-won
 ---
 # fable-traces
@@ -57,4 +56,4 @@ Apache 2.0, following the base model.
 # Disclaimer
-This is a joke. This is not an actual model. Please read the full post first

 license: apache-2.0
 base_model: Qwen/Qwen3-4B-Instruct-2507
 language:
+- en
 library_name: transformers
 tags:
+- qwen3
+- instruct
+- conversational
+- egypt-won
 ---
 # fable-traces
 # Disclaimer
+This is a joke. This is not an actual model. Please read the full post first