wshhghghgh/Qwen3.5_2B-Fable-trained

Fable-5-Qwen3.5-4B

Model Details

Model Description

Fable-5-Qwen3.5-4B is an instruction-tuned language model built on top of Qwen3.5-4B. It is fine-tuned using the Glint-Research/Fable-5-traces dataset, which consists of agent trajectories and reasoning traces designed to improve long-horizon planning, tool usage, and complex task execution.

The model is intended for research purposes involving agentic reasoning, software engineering, planning, and multi-step instruction following.

• Developed by: Glint Research
• Funded by: Not publicly disclosed
• Shared by: Glint Research
• Model type: Causal Language Model (Decoder-only Transformer)
• Language(s): Primarily English (multilingual capabilities inherited from Qwen3.5)
• License: Apache-2.0
• Finetuned from model: Qwen/Qwen3.5-4B

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Model Sources

• Base Model: https://huggingface.co/Qwen/Qwen3.5-4B
• Training Dataset: https://huggingface.co/datasets/Glint-Research/Fable-5-traces

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Uses

Direct Use

This model is suitable for:

• General chat
• Instruction following
• Multi-step reasoning
• Software engineering assistance
• Agent planning
• Tool calling workflows
• Code generation and debugging
• Research on reasoning traces

Downstream Use

Possible downstream applications include:

• Autonomous coding agents
• Research assistants
• Workflow automation
• Interactive tutoring systems
• Multi-agent systems
• Long-horizon planning tasks

Out-of-Scope Use

This model is not intended for:

• Medical diagnosis
• Legal advice
• Financial decision making
• Safety-critical autonomous systems
• High-risk domains requiring guaranteed correctness

Human verification is recommended whenever incorrect outputs could cause harm.

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Bias, Risks, and Limitations

Like other large language models, Fable-5-Qwen3.5-4B may:

• Produce factually incorrect information ("hallucinations")
• Generate biased or culturally dependent responses
• Make reasoning errors despite producing convincing explanations
• Be sensitive to prompt wording
• Inherit limitations from its base model and fine-tuning data

Users should carefully validate outputs before deploying them in production environments.

Recommendations

For best performance:

• Use clear, structured prompts.
• Break complex problems into smaller subtasks.
• Verify generated code before execution.
• Keep a human in the loop for important decisions.

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Getting Started

Example using Transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "YOUR_MODEL_NAME"

tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Explain how quicksort works."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Training Details

Training Data

The model was fine-tuned using the Glint-Research/Fable-5-traces dataset, which contains synthetic reasoning traces and agent trajectories intended to improve multi-step planning and task execution.

Training Procedure

Preprocessing

Training data was formatted into conversational instruction-response pairs compatible with the Qwen chat template.

Training Hyperparameters

Detailed hyperparameters have not been publicly released.

Training likely followed standard supervised fine-tuning (SFT) procedures.

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Evaluation

Testing Data

No standardized benchmark results have been publicly released.

Factors

Performance may vary depending on:

• Prompt quality
• Task complexity
• Context length
• Reasoning depth
• Tool availability

Metrics

No official evaluation metrics have been published.

Results

Qualitative evaluation indicates improved performance on:

• Long-horizon reasoning
• Tool-use planning
• Multi-step coding tasks
• Agent trajectory generation

However, quantitative benchmark results are not currently available.

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Model Examination

The model inherits the multilingual capabilities and general knowledge of Qwen3.5 while specializing in reasoning and agent-oriented tasks through supervised fine-tuning.

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Environmental Impact

Training infrastructure details have not been publicly disclosed.

• Hardware Type: Not publicly disclosed
• Hours used: Not publicly disclosed
• Cloud Provider: Not publicly disclosed
• Compute Region: Not publicly disclosed
• Carbon Emitted: Unknown

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Technical Specifications

Model Architecture

• Architecture: Decoder-only Transformer
• Base Model: Qwen3.5-4B
• Parameter Count: Approximately 2B
• Context Length: Inherited from Qwen3.5 (refer to the base model documentation)

Compute Infrastructure

Hardware

Not publicly disclosed.

Software

Likely trained using the Hugging Face Transformers ecosystem together with PyTorch.

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Citation

If you use this model, please cite both the Qwen base model and the Fable-5-traces dataset.

Example:

@misc{qwen35,
  title={Qwen3.5},
  author={Alibaba Qwen Team},
  year={2025}
}

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More Information

This model is intended primarily for research and experimentation in reasoning, planning, and agentic AI. Users should evaluate the model carefully before deployment in production systems.

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Model Card Authors

Glint Research

Model Card Contact

Please refer to the model repository or organization page for contact information.