Instructions to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DhruvalLabs/qwen3-8b-claude-agentic-fable5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DhruvalLabs/qwen3-8b-claude-agentic-fable5")
model = AutoModelForCausalLM.from_pretrained("DhruvalLabs/qwen3-8b-claude-agentic-fable5", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DhruvalLabs/qwen3-8b-claude-agentic-fable5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DhruvalLabs/qwen3-8b-claude-agentic-fable5

SGLang

How to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DhruvalLabs/qwen3-8b-claude-agentic-fable5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DhruvalLabs/qwen3-8b-claude-agentic-fable5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvalLabs/qwen3-8b-claude-agentic-fable5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DhruvalLabs/qwen3-8b-claude-agentic-fable5 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DhruvalLabs/qwen3-8b-claude-agentic-fable5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DhruvalLabs/qwen3-8b-claude-agentic-fable5 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DhruvalLabs/qwen3-8b-claude-agentic-fable5",
    max_seq_length=2048,
)

Docker Model Runner
How to use DhruvalLabs/qwen3-8b-claude-agentic-fable5 with Docker Model Runner:
```
docker model run hf.co/DhruvalLabs/qwen3-8b-claude-agentic-fable5
```

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.

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

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

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.

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:

@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

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