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

Training Details


Base model	`Qwen/Qwen3-8B`
Dataset	`Glint-Research/Fable-5-traces`
Dataset size	4,665 rows
Method	QLoRA (LoRA fine-tuning on 4-bit quantized model)
LoRA rank	16
LoRA alpha	32
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training steps	580
Batch size	2 × 8 grad accum = 16 effective
Learning rate	2e-4
Sequence length	8192
GPU	NVIDIA RTX 4000 Ada (20GB)
Framework	Unsloth + TRL
Final loss	~0.27

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:

Tool	Count
bash	1,544
edit_file	960
text_response	866
read_file	443
write_file	311
web_search	72
+ others	~200

How to use

Basic inference

python

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)

python

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)

bash

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.

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>

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

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.

License

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

Citation

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

bibtex

@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}
}

Made with ❤️ by DhruvalLabs