qwen3-8b-claude-agentic-fable5

A Qwen3-8B model fine-tuned on Claude Fable-5 agent traces for agentic coding tasks.

Fine-tuned by DhruvalLabs using LoRA on real coding agent trajectories from the Glint-Research/Fable-5-traces dataset.

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What is this model?

This model is fine-tuned to behave like an agentic coding assistant — it reasons step by step inside <think> tags before taking any action, then calls the appropriate tool to complete the task.

Unlike a standard chat model that just replies with text, this model:

• Thinks before acting — every response includes a reasoning chain
• Calls tools correctly — uses bash, read_file, write_file, edit_file, web_search and more
• Works multi-step — plans and executes complex tasks autonomously
• Follows the Fable-5 agent style — trained on real Claude Fable-5 coding sessions

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Training Details

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What it learned

The model was trained on real Fable-5 (Claude) coding agent sessions. Each training example contains:

• A coding task from a real user
• The agent's step-by-step reasoning (<think> block)
• The tool call the agent made (bash, read_file, write_file, etc.)

Tools present in training data:

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How to use

Basic inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DhruvalLabs/qwen3-8b-claude-agentic-fable5")

messages = [
    {
        "role": "system",
        "content": (
            "You are an expert agentic coding assistant. "
            "Before every action, reason carefully inside <think>...</think> tags. "
            "Then call the appropriate tool to complete the task step by step."
        )
    },
    {
        "role": "user",
        "content": "/think\nRead the file main.py and summarize what it does."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=500,
    temperature=0.7,
    do_sample=True,
)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

With Unsloth (faster, less VRAM)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name     = "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
    max_seq_length = 8192,
    load_in_4bit   = True,
)
FastLanguageModel.for_inference(model)

With vLLM (for serving as API)

python -m vllm.entrypoints.openai.api_server \
    --model DhruvalLabs/qwen3-8b-claude-agentic-fable5 \
    --port 8000 \
    --max-model-len 8192

Then connect any OpenAI-compatible client to http://localhost:8000/v1.

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Example output

Input:

Check if Node.js is installed and what version it is.

Output:

<tool_call>
{"name": "bash", "arguments": {"command": "node --version 2>/dev/null || echo 'Node.js not installed'"}}
</tool_call>

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Tips for best results

• Always add /think at the start of your user message to trigger reasoning mode
• Use a system prompt that tells the model it's an agentic assistant
• Set temperature=0.7 for a good balance of creativity and consistency
• Set max_new_tokens to at least 400 for complex tasks

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Dataset

Trained on Glint-Research/Fable-5-traces — a dataset of real Fable-5 (Claude) coding agent traces converted to Qwen3 chat format.

Data preprocessing and format conversion done by DhruvalLabs.

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License

This model inherits the Apache 2.0 license from the base Qwen3-8B model. Free to use for personal, research, and commercial purposes.

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Citation

If you use this model in your research or project, please cite:

@misc{dhruval2026qwen3fable5,
  author    = {DhruvalLabs},
  title     = {qwen3-8b-claude-agentic-fable5: Qwen3-8B Fine-tuned on Fable-5 Agent Traces},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/DhruvalLabs/qwen3-8b-claude-agentic-fable5}
}

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Made with ❤️ by DhruvalLabs