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Model: DhruvalLabs/qwen3-8b-claude-agentic-fable5 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- qwen3
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- agentic
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- coding-agent
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- tool-use
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- finetuned
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- lora
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- unsloth
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- fable5
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- claude
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# qwen3-8b-claude-agentic-fable5
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**A Qwen3-8B model fine-tuned on Claude Fable-5 agent traces for agentic coding tasks.**
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Fine-tuned by **DhruvalLabs** using LoRA on real coding agent trajectories from the
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[Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) dataset.
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---
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## What is this model?
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This model is fine-tuned to behave like an **agentic coding assistant** — it reasons step by step
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inside `<think>` tags before taking any action, then calls the appropriate tool to complete the task.
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Unlike a standard chat model that just replies with text, this model:
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- **Thinks before acting** — every response includes a reasoning chain
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- **Calls tools correctly** — uses `bash`, `read_file`, `write_file`, `edit_file`, `web_search` and more
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- **Works multi-step** — plans and executes complex tasks autonomously
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- **Follows the Fable-5 agent style** — trained on real Claude Fable-5 coding sessions
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---
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## Training Details
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| | |
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|---|---|
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| **Base model** | `Qwen/Qwen3-8B` |
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| **Dataset** | `Glint-Research/Fable-5-traces` |
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| **Dataset size** | 4,665 rows |
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| **Method** | QLoRA (LoRA fine-tuning on 4-bit quantized model) |
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| **LoRA rank** | 16 |
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| **LoRA alpha** | 32 |
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| **Target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Training steps** | 580 |
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| **Batch size** | 2 × 8 grad accum = 16 effective |
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| **Learning rate** | 2e-4 |
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| **Sequence length** | 8192 |
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| **GPU** | NVIDIA RTX 4000 Ada (20GB) |
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| **Framework** | Unsloth + TRL |
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| **Final loss** | ~0.27 |
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---
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## What it learned
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The model was trained on real Fable-5 (Claude) coding agent sessions. Each training example contains:
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- A coding task from a real user
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- The agent's step-by-step reasoning (`<think>` block)
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- The tool call the agent made (`bash`, `read_file`, `write_file`, etc.)
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Tools present in training data:
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| Tool | Count |
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|---|---|
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| bash | 1,544 |
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| edit_file | 960 |
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| text_response | 866 |
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| read_file | 443 |
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| write_file | 311 |
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| web_search | 72 |
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| + others | ~200 |
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---
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## How to use
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### Basic inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"DhruvalLabs/qwen3-8b-claude-agentic-fable5",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("DhruvalLabs/qwen3-8b-claude-agentic-fable5")
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messages = [
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{
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"role": "system",
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"content": (
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"You are an expert agentic coding assistant. "
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"Before every action, reason carefully inside <think>...</think> tags. "
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"Then call the appropriate tool to complete the task step by step."
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)
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},
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{
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"role": "user",
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"content": "/think\nRead the file main.py and summarize what it does."
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}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to("cuda")
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outputs = model.generate(
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input_ids=inputs,
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max_new_tokens=500,
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temperature=0.7,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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### With Unsloth (faster, less VRAM)
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
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max_seq_length = 8192,
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load_in_4bit = True,
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)
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FastLanguageModel.for_inference(model)
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```
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### With vLLM (for serving as API)
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model DhruvalLabs/qwen3-8b-claude-agentic-fable5 \
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--port 8000 \
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--max-model-len 8192
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```
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Then connect any OpenAI-compatible client to `http://localhost:8000/v1`.
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---
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## Example output
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**Input:**
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```
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Check if Node.js is installed and what version it is.
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```
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**Output:**
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```
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<tool_call>
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{"name": "bash", "arguments": {"command": "node --version 2>/dev/null || echo 'Node.js not installed'"}}
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</tool_call>
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```
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---
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## Tips for best results
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- Always add `/think` at the start of your user message to trigger reasoning mode
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- Use a system prompt that tells the model it's an agentic assistant
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- Set `temperature=0.7` for a good balance of creativity and consistency
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- Set `max_new_tokens` to at least 400 for complex tasks
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---
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## Dataset
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Trained on [Glint-Research/Fable-5-traces](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) —
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a dataset of real Fable-5 (Claude) coding agent traces converted to Qwen3 chat format.
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Data preprocessing and format conversion done by **DhruvalLabs**.
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---
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## License
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This model inherits the **Apache 2.0** license from the base Qwen3-8B model.
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Free to use for personal, research, and commercial purposes.
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---
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## Citation
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If you use this model in your research or project, please cite:
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```bibtex
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@misc{dhruval2026qwen3fable5,
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author = {DhruvalLabs},
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title = {qwen3-8b-claude-agentic-fable5: Qwen3-8B Fine-tuned on Fable-5 Agent Traces},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/DhruvalLabs/qwen3-8b-claude-agentic-fable5}
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}
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```
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---
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*Made with ❤️ by DhruvalLabs*
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