--- license: apache-2.0 base_model: Qwen/Qwen3-8B tags: - qwen3 - agentic - coding-agent - tool-use - finetuned - lora - unsloth - fable5 - claude language: - en pipeline_tag: text-generation library_name: transformers --- # 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](https://huggingface.co/datasets/Glint-Research/Fable-5-traces) dataset. --- ## What is this model? This model is fine-tuned to behave like an **agentic coding assistant** — it reasons step by step inside ` ` 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 (` ` 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 ... 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:** ``` {"name": "bash", "arguments": {"command": "node --version 2>/dev/null || echo 'Node.js not installed'"}} ``` --- ## 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](https://huggingface.co/datasets/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*
